{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T19:13:51Z","timestamp":1774379631934,"version":"3.50.1"},"reference-count":364,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T00:00:00Z","timestamp":1712880000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T00:00:00Z","timestamp":1712880000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100002341","name":"Academy of Finland","doi-asserted-by":"publisher","award":["321991"],"award-info":[{"award-number":["321991"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002341","name":"Academy of Finland","doi-asserted-by":"publisher","award":["339612"],"award-info":[{"award-number":["339612"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002341","name":"Academy of Finland","doi-asserted-by":"publisher","award":["321980"],"award-info":[{"award-number":["321980"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100018701","name":"HORIZON EUROPE Food, Bioeconomy, Natural Resources, Agriculture and Environment","doi-asserted-by":"publisher","award":["101081642"],"award-info":[{"award-number":["101081642"]}],"id":[{"id":"10.13039\/100018701","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Planktonic organisms including phyto-, zoo-, and mixoplankton are key components of aquatic ecosystems and respond quickly to changes in the environment, therefore their monitoring is vital to follow and understand these changes. Advances in imaging technology have enabled novel possibilities to study plankton populations, but the manual classification of images is time consuming and expert-based, making such an approach unsuitable for large-scale application and urging for automatic solutions for the analysis, especially recognizing the plankton species from images. Despite the extensive research done on automatic plankton recognition, the latest cutting-edge methods have not been widely adopted for operational use. In this paper, a comprehensive survey on existing solutions for automatic plankton recognition is presented. First, we identify the most notable challenges that make the development of plankton recognition systems difficult and restrict the deployment of these systems for operational use. Then, we provide a detailed description of solutions found in plankton recognition literature. Finally, we propose a workflow to identify the specific challenges in new datasets and the recommended approaches to address them. Many important challenges remain unsolved including the following: (1) the domain shift between the datasets hindering the development of an imaging instrument independent plankton recognition system, (2) the difficulty to identify and process the images of previously unseen classes and non-plankton particles, and (3) the uncertainty in expert annotations that affects the training of the machine learning models. To build harmonized instrument and location agnostic methods for operational purposes these challenges should be addressed in future research.<\/jats:p>","DOI":"10.1007\/s10462-024-10745-y","type":"journal-article","created":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T06:02:09Z","timestamp":1712901729000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Survey of automatic plankton image recognition: challenges, existing solutions and future perspectives"],"prefix":"10.1007","volume":"57","author":[{"given":"Tuomas","family":"Eerola","sequence":"first","affiliation":[]},{"given":"Daniel","family":"Batrakhanov","sequence":"additional","affiliation":[]},{"given":"Nastaran Vatankhah","family":"Barazandeh","sequence":"additional","affiliation":[]},{"given":"Kaisa","family":"Kraft","sequence":"additional","affiliation":[]},{"given":"Lumi","family":"Haraguchi","sequence":"additional","affiliation":[]},{"given":"Lasse","family":"Lensu","sequence":"additional","affiliation":[]},{"given":"Sanna","family":"Suikkanen","sequence":"additional","affiliation":[]},{"given":"Jukka","family":"Sepp\u00e4l\u00e4","sequence":"additional","affiliation":[]},{"given":"Timo","family":"Tamminen","sequence":"additional","affiliation":[]},{"given":"Heikki","family":"K\u00e4lvi\u00e4inen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,12]]},"reference":[{"issue":"6","key":"10745_CR1","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1504\/IJCVR.2018.095584","volume":"8","author":"H Al-Barazanchi","year":"2018","unstructured":"Al-Barazanchi H, Verma A, Wang SX (2018) Intelligent plankton image classification with deep learning. Int J Comput Vision Robot 8(6):561\u2013571","journal-title":"Int J Comput Vision Robot"},{"key":"10745_CR2","doi-asserted-by":"crossref","unstructured":"Al-Barazanchi HA, Verma A, Wang S (2015a) Performance evaluation of hybrid CNN for SIPPER plankton image calssification. In: International conference on image information processing (ICIIP), IEEE, pp 551\u2013556","DOI":"10.1109\/ICIIP.2015.7460262"},{"key":"10745_CR3","unstructured":"Al-Barazanchi HA, Verma A, Wang S (2015b) Plankton image classification using convolutional neural networks. In: International conference on image processing, computer vision, and pattern recognition (IPCV), pp 455\u2013461"},{"key":"10745_CR4","doi-asserted-by":"crossref","unstructured":"Alfano PD, Rando M, Letizia M, et\u00a0al (2022) Efficient unsupervised learning for plankton images. arXiv preprint arXiv:2209.06726","DOI":"10.1109\/ICPR56361.2022.9956360"},{"issue":"14","key":"10745_CR5","doi-asserted-by":"publisher","first-page":"2219","DOI":"10.3390\/w14142219","volume":"14","author":"S Ali","year":"2022","unstructured":"Ali S, Khan Z, Hussain A et al (2022) Computer vision based deep learning approach for the detection and classification of algae species using microscopic images. Water 14(14):2219","journal-title":"Water"},{"key":"10745_CR6","doi-asserted-by":"publisher","first-page":"250","DOI":"10.3389\/fmars.2019.00250","volume":"6","author":"CR Anderson","year":"2019","unstructured":"Anderson CR, Berdalet E, Kudela RM et al (2019) Scaling up from regional case studies to a global harmful algal bloom observing system. Front Marine Sci 6:250","journal-title":"Front Marine Sci"},{"key":"10745_CR364","first-page":"115917","volume":"87","author":"J \u00c4rje","year":"2020","unstructured":"\u00c4rje J, Raitoharju J, Iosifidis A et al (2020) Human experts vs. machines in taxa recognition. Signal Process: Image Commun 87:115917","journal-title":"Signal Process: Image Commun"},{"key":"10745_CR7","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1038\/nature04159","volume":"437","author":"KR Arrigo","year":"2005","unstructured":"Arrigo KR (2005) Marine microorganisms and global nutrient cycles. Nature 437:349\u2013355","journal-title":"Nature"},{"key":"10745_CR8","unstructured":"Aurelia, Luo J, Josette-BoozAllen, et\u00a0al (2014) National Data Science Bowl. https:\/\/kaggle.com\/competitions\/datasciencebowl"},{"key":"10745_CR9","doi-asserted-by":"crossref","unstructured":"Bachimanchi H, Pinder MI, Robert C, et\u00a0al (2023) Deep-learning-powered data analysis in plankton ecology. arXiv preprint arXiv:2309.08500","DOI":"10.1002\/lol2.10392"},{"key":"10745_CR10","doi-asserted-by":"crossref","unstructured":"Badreldeen Bdawy\u00a0Mohamed O, Eerola T, Kraft K, et\u00a0al. (2022) Open-set plankton recognition using similarity learning. In: International symposium on visual computing (ISVC)","DOI":"10.1007\/978-3-031-20713-6_13"},{"issue":"12","key":"10745_CR11","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481\u20132495","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10745_CR12","unstructured":"Bao H, Dong L, Piao S, et\u00a0al (2021) BEiT: BERT pre-training of image transformers. In: International conference on learning representations"},{"key":"10745_CR13","doi-asserted-by":"crossref","unstructured":"Barsanti L, Birindelli L, Gualtieri P (2021) Water monitoring by means of digital microscopy identification and classification of microalgae. Processes & Impacts, Environmental Science","DOI":"10.1039\/D1EM00258A"},{"key":"10745_CR14","doi-asserted-by":"publisher","first-page":"102401","DOI":"10.1016\/j.hal.2023.102401","volume":"123","author":"R Barua","year":"2023","unstructured":"Barua R, Sanborn D, Nyman L et al (2023) In situ digital holographic microscopy for rapid detection and monitoring of the harmful dinoflagellate, karenia brevis. Harmful Algae 123:102401","journal-title":"Harmful Algae"},{"key":"10745_CR15","doi-asserted-by":"crossref","unstructured":"Bay H, Tuytelaars T, Van\u00a0Gool L (2006) Surf: Speeded up robust features. In: European Conference on Computer Vision (ECCV), Springer, pp 404\u2013417","DOI":"10.1007\/11744023_32"},{"key":"10745_CR16","unstructured":"Beijbom O, Hoffman J, Yao E, et\u00a0al (2015) Quantification in-the-wild: Data-sets and baselines. arXiv preprint arXiv:1510.04811"},{"issue":"12","key":"10745_CR17","doi-asserted-by":"publisher","first-page":"1351","DOI":"10.1093\/plankt\/fbn092","volume":"30","author":"JL Bell","year":"2008","unstructured":"Bell JL, Hopcroft RR (2008) Assessment of zooimage as a tool for the classification of zooplankton. J Plankton Res 30(12):1351\u20131367","journal-title":"J Plankton Res"},{"issue":"1","key":"10745_CR18","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/s10994-009-5152-4","volume":"79","author":"S Ben-David","year":"2010","unstructured":"Ben-David S, Blitzer J, Crammer K et al (2010) A theory of learning from different domains. Mach Learn 79(1):151\u2013175","journal-title":"Mach Learn"},{"key":"10745_CR19","doi-asserted-by":"crossref","unstructured":"Benammar N, Kahil H, Titah A, et\u00a0al (2021) Improving 3d plankton image classification with c3d2 architecture and context metadata. In: International conference on innovations in bio-inspired computing and applications, Springer, pp 170\u2013182","DOI":"10.1007\/978-3-030-96299-9_17"},{"key":"10745_CR20","doi-asserted-by":"crossref","unstructured":"Bendale A, Boult T (2016) Towards open set deep networks. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp 1563\u20131572","DOI":"10.1109\/CVPR.2016.173"},{"key":"10745_CR21","doi-asserted-by":"publisher","first-page":"172","DOI":"10.5670\/oceanog.2007.63","volume":"20","author":"MC Benfield","year":"2007","unstructured":"Benfield MC, Grosjean P, Culverhouse PF et al (2007) Rapid: research on automated plankton identification. Oceanography 20:172\u2013187","journal-title":"Oceanography"},{"key":"10745_CR22","unstructured":"Bernhard B, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: Workshop on computational learning theory. association for computing machinery, p 144\u2013152"},{"issue":"5","key":"10745_CR23","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1111\/jpy.12767","volume":"54","author":"B Beszteri","year":"2018","unstructured":"Beszteri B, Allen C, Almandoz GO et al (2018) Quantitative comparison of taxa and taxon concepts in the diatom genus fragilariopsis: a case study on using slide scanning, multiexpert image annotation, and image analysis in taxonomy1. J Phycol 54(5):703\u2013719","journal-title":"J Phycol"},{"key":"10745_CR24","doi-asserted-by":"publisher","first-page":"e0127121","DOI":"10.1371\/journal.pone.0127121","volume":"10","author":"H Bi","year":"2015","unstructured":"Bi H, Guo Z, Benfield MC et al (2015) A semi-automated image analysis procedure for in situ plankton imaging systems. PLOS ONE 10:e0127121","journal-title":"PLOS ONE"},{"key":"10745_CR25","doi-asserted-by":"crossref","unstructured":"Blaschko MB, Holness G, Mattar MA, et\u00a0al (2005) Automatic in situ identification of plankton. In: Workshops on applications of computer vision (WACV), IEEE, pp 79\u201386","DOI":"10.1109\/ACVMOT.2005.29"},{"key":"10745_CR26","doi-asserted-by":"crossref","unstructured":"Bochinski E, Bacha G, Eiselein V, et\u00a0al (2018) Deep active learning for in situ plankton classification. In: International conference on pattern recognition (ICPR), pp 5\u201315","DOI":"10.1007\/978-3-030-05792-3_1"},{"issue":"4","key":"10745_CR27","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1002\/cyto.990150403","volume":"15","author":"L Boddy","year":"1994","unstructured":"Boddy L, Morris C, Wilkins M et al (1994) Neural network analysis of flow cytometric data for 40 marine phytoplankton species. Cytom: J Int Soci Anal Cytol 15(4):283\u2013293","journal-title":"Cytom: J Int Soci Anal Cytol"},{"key":"10745_CR28","doi-asserted-by":"publisher","first-page":"47","DOI":"10.3354\/meps195047","volume":"195","author":"L Boddy","year":"2000","unstructured":"Boddy L, Morris C, Wilkins M et al (2000) Identification of 72 phytoplankton species by radial basis function neural network analysis of flow cytometric data. Mar Ecol Prog Ser 195:47\u201359","journal-title":"Mar Ecol Prog Ser"},{"key":"10745_CR29","doi-asserted-by":"publisher","first-page":"753","DOI":"10.3390\/app7080753","volume":"7","author":"G Bueno","year":"2017","unstructured":"Bueno G, Deniz O, Pedraza A et al (2017) Automated diatom classification (part a): handcrafted feature approaches. Appl Sci 7:753","journal-title":"Appl Sci"},{"key":"10745_CR30","doi-asserted-by":"crossref","unstructured":"Bure\u0161 J, Eerola T, Lensu L, et\u00a0al (2021) Plankton recognition in images with varying size. In: International conference on pattern recognition (ICPR) workshops and challenges","DOI":"10.1007\/978-3-030-68780-9_11"},{"key":"10745_CR31","doi-asserted-by":"publisher","first-page":"102811","DOI":"10.1016\/j.algal.2022.102811","volume":"66","author":"H Cai","year":"2022","unstructured":"Cai H, Shan S, Wang X (2022) Rapid detection for optical micrograph of plankton in ballast water based on neural network. Algal Res 66:102811","journal-title":"Algal Res"},{"key":"10745_CR32","doi-asserted-by":"crossref","unstructured":"Cai S, Zuo W, Zhang L (2017) Higher-order integration of hierarchical convolutional activations for fine-grained visual categorization. In: International conference on computer vision (ICCV), pp 511\u2013520","DOI":"10.1109\/ICCV.2017.63"},{"key":"10745_CR33","doi-asserted-by":"publisher","first-page":"1440","DOI":"10.1093\/icesjms\/fsaa029","volume":"77","author":"RW Campbell","year":"2020","unstructured":"Campbell RW, Roberts P, Jaffe J (2020) The prince william sound plankton camera: a profiling in situ observatory of plankton and particulates. ICES J Mar Sci 77:1440\u20131455","journal-title":"ICES J Mar Sci"},{"issue":"1","key":"10745_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2733381","volume":"10","author":"RJ Campello","year":"2015","unstructured":"Campello RJ, Moulavi D, Zimek A et al (2015) Hierarchical density estimates for data clustering, visualization, and outlier detection. ACM Trans Knowl Discovery Data (TKDD) 10(1):1\u201351","journal-title":"ACM Trans Knowl Discovery Data (TKDD)"},{"key":"10745_CR35","doi-asserted-by":"crossref","unstructured":"Chang L, Wang R, Zheng H, et\u00a0al (2016) Phytoplankton feature extraction from microscopic images based on surf-pca. In: OCEANS Conference, IEEE, pp 1\u20134","DOI":"10.1109\/OCEANSAP.2016.7485699"},{"key":"10745_CR36","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO et al (2002) Smote: synthetic minority over-sampling technique. J Artif Intell Res 16:321\u2013357","journal-title":"J Artif Intell Res"},{"key":"10745_CR37","unstructured":"Chen T, Kornblith S, Norouzi M, et\u00a0al (2020) A simple framework for contrastive learning of visual representations. In: International conference on machine learning, pp 1597\u20131607"},{"key":"10745_CR38","doi-asserted-by":"publisher","first-page":"18048","DOI":"10.1021\/acs.est.3c00253","volume":"57","author":"Z Chen","year":"2023","unstructured":"Chen Z, Du M, Yang XD et al (2023) Deep-learning-based automated tracking and counting of living plankton in natural aquatic environments. Environ Sci Technol 57:18048\u201318057","journal-title":"Environ Sci Technol"},{"key":"10745_CR39","doi-asserted-by":"crossref","unstructured":"Cheng K, Cheng X, Hao Q (2018) A review of feature extraction technologies for plankton images. In: International conference on information hiding and image processing (IHIP), pp 48\u201356","DOI":"10.1145\/3292425.3293462"},{"key":"10745_CR40","doi-asserted-by":"publisher","first-page":"e0219570","DOI":"10.1371\/journal.pone.0219570","volume":"14","author":"K Cheng","year":"2019","unstructured":"Cheng K, Cheng X, Wang Y et al (2019) Enhanced convolutional neural network for plankton identification and enumeration. PLoS ONE 14:e0219570","journal-title":"PLoS ONE"},{"issue":"9","key":"10745_CR41","doi-asserted-by":"publisher","first-page":"2592","DOI":"10.3390\/s20092592","volume":"20","author":"X Cheng","year":"2020","unstructured":"Cheng X, Ren Y, Cheng K et al (2020) Method for training convolutional neural networks for in situ plankton image recognition and classification based on the mechanisms of the human eye. Sensors 20(9):2592","journal-title":"Sensors"},{"key":"10745_CR42","doi-asserted-by":"crossref","unstructured":"Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. In: Conference on computer vision and pattern recognition (CVPR), pp 1251\u20131258","DOI":"10.1109\/CVPR.2017.195"},{"key":"10745_CR43","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.pocean.2017.10.014","volume":"166","author":"F Colas","year":"2018","unstructured":"Colas F, Tardivel M, Perchoc J et al (2018) The ZooCAM, a new in-flow imaging system for fast onboard counting, sizing and classification of fish eggs and metazooplankton. Prog Oceanogr 166:54\u201365","journal-title":"Prog Oceanogr"},{"key":"10745_CR44","doi-asserted-by":"publisher","first-page":"e26066","DOI":"10.7554\/eLife.26066","volume":"6","author":"S Colin","year":"2017","unstructured":"Colin S, Coelho LP, Sunagawa S et al (2017) Quantitative 3d-imaging for cell biology and ecology of environmental microbial eukaryotes. Elife 6:e26066","journal-title":"Elife"},{"issue":"11","key":"10745_CR45","first-page":"2656","volume":"16","author":"P Coltelli","year":"2014","unstructured":"Coltelli P, Barsanti L, Evangelista V et al (2014) Water monitoring: automated and real time identification and classification of algae using digital microscopy. Environ Sci: Processes Impacts 16(11):2656\u20132665","journal-title":"Environ Sci: Processes Impacts"},{"key":"10745_CR46","doi-asserted-by":"publisher","first-page":"868420","DOI":"10.3389\/fmars.2022.868420","volume":"9","author":"J Conradt","year":"2022","unstructured":"Conradt J, B\u00f6rner G, L\u00f3pez-Urrutia \u00c1 et al (2022) Automated plankton classification with a dynamic optimization and adaptation cycle. Front Mar Sci 9:868420","journal-title":"Front Mar Sci"},{"key":"10745_CR47","doi-asserted-by":"publisher","first-page":"2124","DOI":"10.3390\/s16122124","volume":"16","author":"L Corgnati","year":"2016","unstructured":"Corgnati L, Marini S, Mazzei L et al (2016) Looking inside the ocean: toward an autonomous imaging system for monitoring gelatinous zooplankton. Sensors 16:2124","journal-title":"Sensors"},{"key":"10745_CR48","doi-asserted-by":"crossref","unstructured":"Corr\u00eaa I, Drews P, de\u00a0Souza MS, et\u00a0al (2016) Supervised microalgae classification in imbalanced dataset. In: Brazilian conference on intelligent systems (BRACIS), IEEE, pp 49\u201354","DOI":"10.1109\/BRACIS.2016.020"},{"key":"10745_CR49","doi-asserted-by":"crossref","unstructured":"Correa I, Drews P, Botelho S, et\u00a0al (2017) Deep learning for microalgae classification. In: International conference on machine learning and applications (ICMLA), pp 20\u201325","DOI":"10.1109\/ICMLA.2017.0-183"},{"key":"10745_CR50","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/BF00994018","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273\u2013297","journal-title":"Mach Learn"},{"key":"10745_CR51","unstructured":"Cosgriff R (1960) Identification of shape. Ohio State University Research Foundation, Report 820-11"},{"key":"10745_CR52","unstructured":"Cowen R, Sponaugle S, Robinson K, et\u00a0al (2015) PlanktonSet 1.0: Plankton imagery data collected from F.G. Walton smith in straits of florida from 2014-06-03 to 2014-06-06 and used in the 2015 National Data Science Bowl (NCEI Accession 0127422) (National Centers for Environmental Information). https:\/\/doi.org\/10.7289\/v5d21vjd"},{"issue":"2","key":"10745_CR53","doi-asserted-by":"publisher","first-page":"126","DOI":"10.4319\/lom.2008.6.126","volume":"6","author":"RK Cowen","year":"2008","unstructured":"Cowen RK, Guigand CM (2008) In situ ichthyoplankton imaging system (ISIIS): system design and preliminary results. Limnol Oceanogr Methods 6(2):126\u2013132","journal-title":"Limnol Oceanogr Methods"},{"key":"10745_CR54","doi-asserted-by":"crossref","unstructured":"Cui J, Wei B, Wang C, et\u00a0al (2018) Texture and shape information fusion of convolutional neural network for plankton image classification. In: OCEANS Techno-Oceans (OTO), pp 1\u20135","DOI":"10.1109\/OCEANSKOBE.2018.8559156"},{"issue":"4","key":"10745_CR55","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1016\/j.ecoinf.2007.07.001","volume":"2","author":"PF Culverhouse","year":"2007","unstructured":"Culverhouse PF (2007) Human and machine factors in algae monitoring performance. Eco Inform 2(4):361\u2013366","journal-title":"Eco Inform"},{"key":"10745_CR56","doi-asserted-by":"publisher","first-page":"17","DOI":"10.3354\/meps247017","volume":"247","author":"PF Culverhouse","year":"2003","unstructured":"Culverhouse PF, Williams R, Reguera B et al (2003) Do experts make mistakes? a comparison of human and machine indentification of dinoflagellates. Mar Ecol Prog Ser 247:17\u201325","journal-title":"Mar Ecol Prog Ser"},{"key":"10745_CR57","doi-asserted-by":"crossref","unstructured":"Dai J, Wang R, Zheng H, et\u00a0al (2016a) Zooplanktonet: deep convolutional network for zooplankton classification. In: OCEANS Conference, pp 1\u20136","DOI":"10.1109\/OCEANSAP.2016.7485680"},{"key":"10745_CR58","doi-asserted-by":"crossref","unstructured":"Dai J, Yu Z, Zheng H, et\u00a0al (2016b) A hybrid convolutional neural network for plankton classification. In: Asian conference on computer vision (ACCV), Springer, pp 102\u2013114","DOI":"10.1007\/978-3-319-54526-4_8"},{"issue":"7951","key":"10745_CR59","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1038\/s41586-023-05760-y","volume":"615","author":"Y Dai","year":"2023","unstructured":"Dai Y, Yang S, Zhao D et al (2023) Coastal phytoplankton blooms expand and intensify in the 21st century. Nature 615(7951):280\u2013284","journal-title":"Nature"},{"key":"10745_CR60","doi-asserted-by":"crossref","unstructured":"Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp 886\u2013893","DOI":"10.1109\/CVPR.2005.177"},{"key":"10745_CR61","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1126\/science.257.5067.230","volume":"257","author":"CS Davis","year":"1992","unstructured":"Davis CS, Gallager SM, Solow AR (1992) Microaggregations of oceanic plankton observed by towed video microscopy. Science 257:230\u2013232","journal-title":"Science"},{"key":"10745_CR62","doi-asserted-by":"publisher","first-page":"77","DOI":"10.3354\/meps284077","volume":"284","author":"CS Davis","year":"2004","unstructured":"Davis CS, Hu Q, Gallager SM et al (2004) Real-time observation of taxa-specific plankton distributions: an optical sampling method. Mar Ecol Prog Ser 284:77\u201396","journal-title":"Mar Ecol Prog Ser"},{"issue":"2","key":"10745_CR63","doi-asserted-by":"publisher","first-page":"59","DOI":"10.4319\/lom.2005.3.59","volume":"3","author":"CS Davis","year":"2005","unstructured":"Davis CS, Thwaites FT, Gallager SM et al (2005) A three-axis fast-tow digital video plankton recorder for rapid surveys of plankton taxa and hydrography. Limnol Oceanogr Meth 3(2):59\u201374","journal-title":"Limnol Oceanogr Meth"},{"key":"10745_CR64","doi-asserted-by":"publisher","first-page":"6237","DOI":"10.1126\/science.1261605","volume":"348","author":"C De Vargas","year":"2015","unstructured":"De Vargas C, Audic S, Henry N et al (2015) Eukaryotic plankton diversity in the sunlit ocean. Science 348:6237","journal-title":"Science"},{"key":"10745_CR65","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, et\u00a0al (2009) Imagenet: a large-scale hierarchical image database. In: Conference on computer vision and pattern recognition (CVPR), IEEE, pp 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"10745_CR66","doi-asserted-by":"crossref","unstructured":"Deng J, Guo J, Xue N, et\u00a0al (2019) ArcFace: additive angular margin loss for deep face recognition. In: Conference on computer vision and pattern recognition (CVPR), pp 4690\u20134699","DOI":"10.1109\/CVPR.2019.00482"},{"issue":"1","key":"10745_CR67","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.ecoinf.2011.09.001","volume":"7","author":"I Dimitrovski","year":"2012","unstructured":"Dimitrovski I, Kocev D, Loskovska S et al (2012) Hierarchical classification of diatom images using ensembles of predictive clustering trees. Eco Inform 7(1):19\u201329","journal-title":"Eco Inform"},{"key":"10745_CR68","doi-asserted-by":"crossref","unstructured":"Ding H, Wei B, Tang N, et\u00a0al (2018) Plankton image classification via multi-class imbalanced learning. In: OCEANS Techno-Oceans (OTO), IEEE, pp 1\u20136","DOI":"10.1109\/OCEANSKOBE.2018.8559238"},{"issue":"21","key":"10745_CR69","doi-asserted-by":"publisher","first-page":"14871","DOI":"10.1007\/s11042-019-07856-y","volume":"79","author":"H Ding","year":"2020","unstructured":"Ding H, Wei B, Gu Z et al (2020) Ka-ensemble: towards imbalanced image classification ensembling under-sampling and over-sampling. Multim Tools Appl 79(21):14871\u201314888","journal-title":"Multim Tools Appl"},{"key":"10745_CR70","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, et\u00a0al (2021) An image is worth 16x16 words: Transformers for image recognition at scale. In: International conference on learning representations"},{"issue":"4","key":"10745_CR71","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1007\/s13173-013-0121-y","volume":"19","author":"P Drews","year":"2013","unstructured":"Drews P, Colares RG, Machado P et al (2013) Microalgae classification using semi-supervised and active learning based on Gaussian mixture models. J Braz Comput Soc 19(4):411\u2013422","journal-title":"J Braz Comput Soc"},{"key":"10745_CR72","doi-asserted-by":"crossref","unstructured":"Du A, Gu Z, Yu Z, et\u00a0al (2020) Plankton image classification using deep convolutional neural networks with second-order features. In: Global oceans 2020: Singapore\u2013US Gulf Coast, IEEE, pp 1\u20135","DOI":"10.1109\/IEEECONF38699.2020.9389034"},{"key":"10745_CR73","doi-asserted-by":"publisher","DOI":"10.1142\/4907","volume-title":"Automatic diatom identification","author":"H Du Buf","year":"2002","unstructured":"Du Buf H, Bayer MM (2002) Automatic diatom identification. World Scientific, Singapore"},{"key":"10745_CR74","doi-asserted-by":"crossref","unstructured":"Du\u00a0Buf H, Bayer M, Droop S, et\u00a0al (1999) Diatom identification: a double challenge called adiac. In: International conference on image analysis and processing (CAIP), IEEE, pp 734\u2013739","DOI":"10.1109\/ICIAP.1999.797682"},{"issue":"4","key":"10745_CR75","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1002\/(SICI)1097-0320(19991201)37:4<247::AID-CYTO1>3.0.CO;2-9","volume":"37","author":"GB Dubelaar","year":"1999","unstructured":"Dubelaar GB, Gerritzen PL, Beeker AE et al (1999) Design and first results of CytoBuoy: a wireless flow cytometer for in situ analysis of marine and fresh waters. Cytometry: J Int Soci Anal Cytol 37(4):247\u2013254","journal-title":"Cytometry: J Int Soci Anal Cytol"},{"issue":"5","key":"10745_CR76","doi-asserted-by":"publisher","first-page":"2687","DOI":"10.1109\/TCSVT.2021.3080920","volume":"32","author":"SR Dubey","year":"2021","unstructured":"Dubey SR (2021) A decade survey of content based image retrieval using deep learning. IEEE Trans Circuits Syst Video Technol 32(5):2687\u20132704","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"1","key":"10745_CR77","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1145\/361237.361242","volume":"15","author":"RO Duda","year":"1972","unstructured":"Duda RO, Hart PE (1972) Use of the hough transformation to detect lines and curves in pictures. Commun ACM 15(1):11\u201315","journal-title":"Commun ACM"},{"key":"10745_CR78","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1186\/s12898-018-0209-5","volume":"18","author":"S Dunker","year":"2018","unstructured":"Dunker S, Boho D, W\u00e4ldchen J et al (2018) Combining high-throughput imaging flow cytometry and deep learning for efficient species and life-cycle stage identification of phytoplankton. BMC Ecol 18:51","journal-title":"BMC Ecol"},{"key":"10745_CR79","unstructured":"Dyomin V, Polovtsev I, Davydova AY (2017) Fast recognition of marine particles in underwater digital holography. In: International symposium on atmospheric and ocean optics: atmospheric physics, p 1046627"},{"issue":"34","key":"10745_CR80","doi-asserted-by":"publisher","first-page":"G300","DOI":"10.1364\/AO.58.00G300","volume":"58","author":"V Dyomin","year":"2019","unstructured":"Dyomin V, Gribenyukov A, Davydova A et al (2019) Holography of particles for diagnostics tasks. Appl Opt 58(34):G300\u2013G310","journal-title":"Appl Opt"},{"key":"10745_CR81","doi-asserted-by":"publisher","first-page":"653","DOI":"10.3389\/fmars.2020.00653","volume":"7","author":"V Dyomin","year":"2020","unstructured":"Dyomin V, Davydova A, Morgalev S et al (2020) Monitoring of plankton spatial and temporal characteristics with the use of a submersible digital holographic camera. Front Mar Sci 7:653","journal-title":"Front Mar Sci"},{"issue":"14","key":"10745_CR82","doi-asserted-by":"publisher","first-page":"4863","DOI":"10.3390\/s21144863","volume":"21","author":"V Dyomin","year":"2021","unstructured":"Dyomin V, Davydova A, Polovtsev I et al (2021) Underwater holographic sensor for plankton studies in situ including accompanying measurements. Sensors 21(14):4863","journal-title":"Sensors"},{"key":"10745_CR83","doi-asserted-by":"crossref","unstructured":"Eerola T, Kraft K, Gr\u00f6nberg O, et\u00a0al (2020) Towards operational phytoplankton recognition with automated high-throughput imaging and compact convolutional neural networks. Ocean Science Discussions, pp 1\u201320","DOI":"10.5194\/os-2020-62"},{"key":"10745_CR84","doi-asserted-by":"publisher","unstructured":"Elineau A, Desnos C, Jalabert L, et\u00a0al (2018) ZooScanNet: plankton images captured with the ZooScan. https:\/\/doi.org\/10.17882\/55741","DOI":"10.17882\/55741"},{"key":"10745_CR85","unstructured":"Elkan C (2001) The foundations of cost-sensitive learning. International Joint Conference on Artificial Intelligence. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp 973\u2013978"},{"key":"10745_CR86","doi-asserted-by":"crossref","unstructured":"Ellen J, Li H, Ohman MD (2015) Quantifying california current plankton samples with efficient machine learning techniques. In: OCEANS Conference, pp 1\u20139","DOI":"10.23919\/OCEANS.2015.7404607"},{"key":"10745_CR87","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1002\/lom3.10324","volume":"17","author":"JS Ellen","year":"2019","unstructured":"Ellen JS, Graff CA, Ohman MD (2019) Improving plankton image classification using context metadata. Limnol Oceanogr Methods 17:439\u2013461","journal-title":"Limnol Oceanogr Methods"},{"issue":"2","key":"10745_CR88","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1007\/BF01501174","volume":"5","author":"R Ellis","year":"1997","unstructured":"Ellis R, Simpson R, Culverhouse PF et al (1997) Committees, collectives and individuals: Expert visual classification by neural network. Neural Comput Appl 5(2):99\u2013105","journal-title":"Neural Comput Appl"},{"issue":"6","key":"10745_CR89","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1093\/plankt\/25.6.669","volume":"25","author":"KV Embleton","year":"2003","unstructured":"Embleton KV, Gibson C, Heaney S (2003) Automated counting of phytoplankton by pattern recognition: a comparison with a manual counting method. J Plankton Res 25(6):669\u2013681","journal-title":"J Plankton Res"},{"key":"10745_CR90","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.mio.2016.04.003","volume":"15","author":"R Faillettaz","year":"2016","unstructured":"Faillettaz R, Picheral M, Luo JY et al (2016) Imperfect automatic image classification successfully describes plankton distribution patterns. Meth Oceanogr 15:60\u201377","journal-title":"Meth Oceanogr"},{"issue":"1","key":"10745_CR91","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1093\/plankt\/fbn098","volume":"31","author":"JA Fernandes","year":"2009","unstructured":"Fernandes JA, Irigoien X, Boyra G et al (2009) Optimizing the number of classes in automated zooplankton classification. J Plankton Res 31(1):19\u201329","journal-title":"J Plankton Res"},{"key":"10745_CR92","doi-asserted-by":"publisher","first-page":"1177","DOI":"10.1016\/j.optlaseng.2011.05.003","volume":"49","author":"A Fern\u00e1ndez","year":"2011","unstructured":"Fern\u00e1ndez A, \u00c1lvarez MX, Bianconi F (2011) Image classification with binary gradient contours. Opt Lasers Eng 49:1177\u20131184","journal-title":"Opt Lasers Eng"},{"issue":"2","key":"10745_CR93","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/s11263-006-0026-8","volume":"75","author":"S Fischer","year":"2007","unstructured":"Fischer S, \u0160roubek F, Perrinet L et al (2007) Self-invertible 2d log-gabor wavelets. Int J Comp Vision (IJCV) 75(2):231\u2013246","journal-title":"Int J Comp Vision (IJCV)"},{"issue":"4","key":"10745_CR94","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1093\/plankt\/fbz026","volume":"41","author":"KJ Flynn","year":"2019","unstructured":"Flynn KJ, Mitra A, Anestis K et al (2019) Mixotrophic protists and a new paradigm for marine ecology: where does plankton research go now? J Plankton Res 41(4):375\u2013391","journal-title":"J Plankton Res"},{"issue":"2","key":"10745_CR95","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1109\/TEC.1961.5219197","volume":"10","author":"H Freeman","year":"1961","unstructured":"Freeman H (1961) On the encoding of arbitrary geometric configurations. IRE Trans Electron Comput 10(2):260\u2013268","journal-title":"IRE Trans Electron Comput"},{"key":"10745_CR96","unstructured":"Ge Z, Liu S, Wang F, et\u00a0al (2021) YOLOX: exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430"},{"issue":"10","key":"10745_CR97","doi-asserted-by":"publisher","first-page":"3614","DOI":"10.1109\/TPAMI.2020.2981604","volume":"43","author":"C Geng","year":"2020","unstructured":"Geng C, Sj Huang, Chen S (2020) Recent advances in open set recognition: a survey. IEEE Trans Patt Anal Mach Intell (PAMI) 43(10):3614\u20133631","journal-title":"IEEE Trans Patt Anal Mach Intell (PAMI)"},{"key":"10745_CR98","doi-asserted-by":"crossref","unstructured":"Geraldes P, Barbosa J, Martins A, et\u00a0al (2019) In situ real-time zooplankton detection and classification. In: OCEANS conference, IEEE, pp 1\u20136","DOI":"10.1109\/OCEANSE.2019.8867552"},{"issue":"1","key":"10745_CR99","first-page":"325","volume":"152","author":"JONV Geronimo","year":"2023","unstructured":"Geronimo JONV, Arguelles ED, Abriol-Santos KJM (2023) Automated classification and identification system for freshwater algae using convolutional neural networks. Phil J Sci 152(1):325\u2013335","journal-title":"Phil J Sci"},{"key":"10745_CR100","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, et\u00a0al (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: conference on computer vision and pattern recognition (CVPR), pp 580\u2013587","DOI":"10.1109\/CVPR.2014.81"},{"issue":"3","key":"10745_CR101","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1002\/lno.12018","volume":"67","author":"PM Glibert","year":"2022","unstructured":"Glibert PM, Mitra A (2022) From webs, loops, shunts, and pumps to microbial multitasking: evolving concepts of marine microbial ecology, the mixoplankton paradigm, and implications for a future ocean. Limnol Oceanogr 67(3):585\u2013597","journal-title":"Limnol Oceanogr"},{"key":"10745_CR102","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1002\/lom3.10151","volume":"15","author":"P Gonz\u00e1lez","year":"2017","unstructured":"Gonz\u00e1lez P, \u00c1lvarez E, D\u00edez J et al (2017) Validation methods for plankton image classification systems. Limnol Oceanogr Methods 15:221\u2013237","journal-title":"Limnol Oceanogr Methods"},{"issue":"4","key":"10745_CR103","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1093\/plankt\/fbz023","volume":"41","author":"P Gonz\u00e1lez","year":"2019","unstructured":"Gonz\u00e1lez P, Casta\u00f1o A, Peacock EE et al (2019) Automatic plankton quantification using deep features. J Plankton Res 41(4):449\u2013463","journal-title":"J Plankton Res"},{"key":"10745_CR104","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, et\u00a0al (2014) Generative adversarial nets. In: Conference on neural information processing systems (NIPS), pp 2672\u20132680"},{"issue":"2","key":"10745_CR105","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1093\/icesjms\/fsab255","volume":"79","author":"M Goodwin","year":"2022","unstructured":"Goodwin M, Halvorsen KT, Jiao L et al (2022) Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook. ICES J Mar Sci 79(2):319\u2013336","journal-title":"ICES J Mar Sci"},{"key":"10745_CR106","doi-asserted-by":"publisher","first-page":"133","DOI":"10.3354\/meps058133","volume":"58","author":"G Gorsky","year":"1989","unstructured":"Gorsky G, Guilbert P, Valenta E (1989) The autonomous image analyzer - enumeration, measurement and identification of marine phytoplankton. Mar Ecol Prog Ser 58:133\u2013142","journal-title":"Mar Ecol Prog Ser"},{"issue":"3","key":"10745_CR107","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1093\/plankt\/fbp124","volume":"32","author":"G Gorsky","year":"2010","unstructured":"Gorsky G, Ohman MD, Picheral M et al (2010) Digital zooplankton image analysis using the zooscan integrated system. J Plankton Res 32(3):285\u2013303","journal-title":"J Plankton Res"},{"key":"10745_CR108","doi-asserted-by":"crossref","unstructured":"Goulart AJH, Morimitsu A, Jacomassi R, et\u00a0al (2021) Deep learning and t-sne projection for plankton images clusterization. In: OCEANS 2021: San Diego\u2013Porto, pp 1\u20134","DOI":"10.23919\/OCEANS44145.2021.9706043"},{"key":"10745_CR109","doi-asserted-by":"crossref","unstructured":"Graham B (2014) Spatially-sparse convolutional neural networks. arXiv preprint arXiv:1409.6070","DOI":"10.5244\/C.29.150"},{"issue":"4","key":"10745_CR110","doi-asserted-by":"publisher","first-page":"518","DOI":"10.1016\/j.icesjms.2004.03.012","volume":"61","author":"P Grosjean","year":"2004","unstructured":"Grosjean P, Picheral M, Warembourg C et al (2004) Enumeration, measurement, and identification of net zooplankton samples using the zooscan digital imaging system. ICES J Mar Sci 61(4):518\u2013525","journal-title":"ICES J Mar Sci"},{"issue":"1","key":"10745_CR111","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/s10872-014-0268-y","volume":"71","author":"MM Grossmann","year":"2015","unstructured":"Grossmann MM, Gallager SM, Mitarai S (2015) Continuous monitoring of near-bottom mesoplankton communities in the east china sea during a series of typhoons. J Oceanogr 71(1):115\u2013124","journal-title":"J Oceanogr"},{"key":"10745_CR112","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","volume":"77","author":"J Gu","year":"2018","unstructured":"Gu J, Wang Z, Kuen J et al (2018) Recent advances in convolutional neural networks. Patt Recogn 77:354\u2013377","journal-title":"Patt Recogn"},{"key":"10745_CR113","unstructured":"Gulrajani I, Lopez-Paz D (2020) In search of lost domain generalization. In: International conference on learning representations"},{"issue":"1","key":"10745_CR114","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1002\/lom3.10402","volume":"19","author":"B Guo","year":"2021","unstructured":"Guo B, Nyman L, Nayak AR et al (2021) Automated plankton classification from holographic imagery with deep convolutional neural networks. Limnol Oceanogr Methods 19(1):21\u201336","journal-title":"Limnol Oceanogr Methods"},{"key":"10745_CR115","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/2518837","volume":"2021","author":"C Guo","year":"2021","unstructured":"Guo C, Wei B, Yu K (2021) Deep transfer learning for biology cross-domain image classification. J Contr Sci Eng 2021:1\u201319","journal-title":"J Contr Sci Eng"},{"key":"10745_CR116","doi-asserted-by":"crossref","unstructured":"Guo G, Lin Q, Chen T, et\u00a0al (2022a) Colorization for in situ marine plankton images. In: European conference on computer vision, Springer, pp 216\u2013232","DOI":"10.1007\/978-3-031-19839-7_13"},{"issue":"9","key":"10745_CR117","doi-asserted-by":"publisher","first-page":"9253","DOI":"10.1007\/s13369-021-05786-2","volume":"46","author":"J Guo","year":"2021","unstructured":"Guo J, Guan J (2021) Classification of marine plankton based on few-shot learning. Arab J Sci Eng 46(9):9253\u20139262","journal-title":"Arab J Sci Eng"},{"key":"10745_CR118","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jher.2021.03.002","volume":"36","author":"J Guo","year":"2021","unstructured":"Guo J, Ma Y, Lee JH (2021) Real-time automated identification of algal bloom species for fisheries management in subtropical coastal waters. J Hydro-Environ Res 36:1\u201332","journal-title":"J Hydro-Environ Res"},{"key":"10745_CR119","doi-asserted-by":"crossref","unstructured":"Guo J, Li W, Guan J, et\u00a0al (2022b) CDFM: a cross-domain few-shot model for marine plankton classification. IET Computer Vision","DOI":"10.1049\/cvi2.12137"},{"key":"10745_CR120","doi-asserted-by":"crossref","unstructured":"Guo X, Liu X, Zhu E, et\u00a0al (2017) Deep clustering with convolutional autoencoders. In: International conference on neural information processing (NIPS), pp 373\u2013382","DOI":"10.1007\/978-3-319-70096-0_39"},{"key":"10745_CR121","doi-asserted-by":"crossref","unstructured":"Han D, Kim J, Kim J (2017) Deep pyramidal residual networks. In: Conference on computer vision and pattern recognition (CVPR), pp 5927\u20135935","DOI":"10.1109\/CVPR.2017.668"},{"key":"10745_CR122","doi-asserted-by":"crossref","unstructured":"Han H, Wang WY, Mao BH (2005) Borderline-smote: a new over-sampling method in imbalanced data sets learning. In: International conference on intelligent computing, pp 878\u2013887","DOI":"10.1007\/11538059_91"},{"issue":"6","key":"10745_CR123","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","volume":"3","author":"RM Haralick","year":"1973","unstructured":"Haralick RM, Shanmugam K, Dinstein IH (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3(6):610\u2013621","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"10745_CR124","doi-asserted-by":"crossref","unstructured":"Hariharan B, Girshick R (2017) Low-shot visual recognition by shrinking and hallucinating features. In: International conference on computer vision (ICCV), pp 3018\u20133027","DOI":"10.1109\/ICCV.2017.328"},{"key":"10745_CR125","unstructured":"Haug ML (2021) Applying active learning techniques in machine learning to minimize labeling effort. Master\u2019s thesis, NTNU"},{"issue":"16","key":"10745_CR126","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1016\/j.ifacol.2021.10.130","volume":"54","author":"ML Haug","year":"2021","unstructured":"Haug ML, Saad A, Stahl A (2021) Ciral: a hybrid active learning framework for plankon taxa labeling. IFAC-PapersOnLine 54(16):450\u2013457","journal-title":"IFAC-PapersOnLine"},{"key":"10745_CR127","doi-asserted-by":"crossref","unstructured":"Haug ML, Saad A, Stahl A (2021b) A combined informative and representative active learning approach for plankton taxa labeling. In: International conference on digital image processing (ICDIP), SPIE, pp 495\u2013503","DOI":"10.1117\/12.2601096"},{"issue":"6","key":"10745_CR128","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1016\/j.tree.2005.03.004","volume":"20","author":"GC Hays","year":"2005","unstructured":"Hays GC, Richardson AJ, Robinson C (2005) Climate change and marine plankton. Trends Ecol Evol 20(6):337\u2013344","journal-title":"Trends Ecol Evol"},{"issue":"9","key":"10745_CR129","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"K He","year":"2015","unstructured":"He K, Zhang X, Ren S et al (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Patt Anal Mach Intell (PAMI) 37(9):1904\u20131916","journal-title":"IEEE Trans Patt Anal Mach Intell (PAMI)"},{"key":"10745_CR130","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, et\u00a0al (2016) Deep residual learning for image recognition. In: The Conference on computer vision and pattern recognition (CVPR), pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"10745_CR131","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Doll\u00e1r P, et\u00a0al (2017) Mask r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp 2961\u20132969","DOI":"10.1109\/ICCV.2017.322"},{"key":"10745_CR132","doi-asserted-by":"crossref","unstructured":"He K, Fan H, Wu Y, et\u00a0al (2020) Momentum contrast for unsupervised visual representation learning. In: Conference on computer vision and pattern recognition (CVPR), pp 9729\u20139738","DOI":"10.1109\/CVPR42600.2020.00975"},{"issue":"22","key":"10745_CR133","doi-asserted-by":"publisher","first-page":"28544","DOI":"10.1007\/s11356-021-12471-2","volume":"28","author":"DW Henrichs","year":"2021","unstructured":"Henrichs DW, Angl\u00e8s S, Gaonkar CC et al (2021) Application of a convolutional neural network to improve automated early warning of harmful algal blooms. Environ Sci Pollut Res 28(22):28544\u201328555","journal-title":"Environ Sci Pollut Res"},{"key":"10745_CR134","doi-asserted-by":"crossref","unstructured":"Hirata NS, Fernandez MA, Lopes RM (2016) Plankton image classification based on multiple segmentations. International Conference on Pattern Recognition (ICPR) Workshops. Computer vision for analysis of underwater imagery (CVAUI), IEEE, pp 55\u201360","DOI":"10.1109\/CVAUI.2016.022"},{"key":"10745_CR135","unstructured":"Ho E, Henriquez B, Yeung J (2018) Flagellates classification via transfer learning. Project Report, Course ECE228 Machine learning for physical applications, University of California San Diego, USA, http:\/\/noiselab.ucsd.edu\/ECE228_2018\/Reports\/Report14.pdf"},{"key":"10745_CR136","unstructured":"Ho TK (1995) Random decision forests. In: International conference on document analysis and recognition (ICDAR), IEEE, pp 278\u2013282"},{"key":"10745_CR137","doi-asserted-by":"crossref","unstructured":"Hoffer E, Ailon N (2015) Deep metric learning using triplet network. In: International workshop on similarity-based pattern recognition, pp 84\u201392","DOI":"10.1007\/978-3-319-24261-3_7"},{"key":"10745_CR138","unstructured":"Howard AG, Zhu M, Chen B, et\u00a0al (2017) Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861"},{"key":"10745_CR139","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Conference on computer vision and pattern recognition (CVPR), pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"issue":"2","key":"10745_CR140","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1109\/TIT.1962.1057692","volume":"8","author":"MK Hu","year":"1962","unstructured":"Hu MK (1962) Visual pattern recognition by moment invariants. IRE Trans Inform Theory 8(2):179\u2013187","journal-title":"IRE Trans Inform Theory"},{"key":"10745_CR141","doi-asserted-by":"publisher","first-page":"21","DOI":"10.3354\/meps295021","volume":"295","author":"Q Hu","year":"2005","unstructured":"Hu Q, Davis C (2005) Automatic plankton image recognition with co-occurrence matrices and support vector machine. Mar Ecol Prog Ser 295:21\u201331","journal-title":"Mar Ecol Prog Ser"},{"key":"10745_CR142","doi-asserted-by":"publisher","first-page":"51","DOI":"10.3354\/meps306051","volume":"306","author":"Q Hu","year":"2006","unstructured":"Hu Q, Davis C (2006) Accurate automatic quantification of taxa-specific plankton abundance using dual classification with correction. Mar Ecol Prog Ser 306:51\u201361","journal-title":"Mar Ecol Prog Ser"},{"key":"10745_CR143","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der\u00a0Maaten L, et\u00a0al (2017) Densely connected convolutional networks. In: Conference on computer vision and pattern recognition (CVPR), pp 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"10745_CR144","unstructured":"Iandola FN, Han S, Moskewicz MW, et\u00a0al (2016) Squeezenet: Alexnet-level accuracy with 50x fewer parameters and $$<$$ 0.5 mb model size. arXiv preprint arXiv:1602.07360"},{"key":"10745_CR145","unstructured":"Ibrahim M (2020) Image clustering for unsupervised analysis of plankton data. Master\u2019s thesis, LUT University, Finland"},{"issue":"9","key":"10745_CR146","doi-asserted-by":"publisher","first-page":"1095","DOI":"10.1016\/S0167-8655(02)00056-9","volume":"23","author":"M Idrissa","year":"2002","unstructured":"Idrissa M, Acheroy M (2002) Texture classification using gabor filters. Patt Recogn Lett 23(9):1095\u20131102","journal-title":"Patt Recogn Lett"},{"key":"10745_CR147","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1146\/annurev-marine-041921-013023","volume":"14","author":"JO Irisson","year":"2022","unstructured":"Irisson JO, Ayata SD, Lindsay DJ et al (2022) Machine learning for the study of plankton and marine snow from images. Ann Rev Mar Sci 14:277\u2013301","journal-title":"Ann Rev Mar Sci"},{"key":"10745_CR148","doi-asserted-by":"crossref","unstructured":"Ito K, Miura K, Aoki T, et\u00a0al (2023) Zooplankton classification using hierarchical attention branch network. In: Asian conference on pattern recognition, Springer, pp 409\u2013419","DOI":"10.1007\/978-3-031-47637-2_31"},{"key":"10745_CR149","unstructured":"Jindal P, Mundra R (2015) Plankton classification using hybrid convolutional network-random forests architectures. Technical Report, Stanford University"},{"key":"10745_CR150","unstructured":"Jocher G (2020) Ultralytics yolov5. https:\/\/github.com\/ultralytics\/yolov5"},{"issue":"2","key":"10745_CR151","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1109\/TIT.1962.1057698","volume":"8","author":"B Julesz","year":"1962","unstructured":"Julesz B (1962) Visual pattern discrimination. IRE Trans Inform Theory 8(2):84\u201392","journal-title":"IRE Trans Inform Theory"},{"key":"10745_CR152","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.cageo.2017.08.011","volume":"109","author":"AS Ke\u00e7eli","year":"2017","unstructured":"Ke\u00e7eli AS, Kaya A, Ke\u00e7eli SU (2017) Classification of radiolarian images with hand-crafted and deep features. Comp Geosci 109:67\u201374","journal-title":"Comp Geosci"},{"key":"10745_CR153","doi-asserted-by":"publisher","first-page":"170013","DOI":"10.1109\/ACCESS.2020.3022242","volume":"8","author":"T Kerr","year":"2020","unstructured":"Kerr T, Clark JR, Fileman ES et al (2020) Collaborative deep learning models to handle class imbalance in flowcam plankton imagery. IEEE Access 8:170013\u2013170032","journal-title":"IEEE Access"},{"key":"10745_CR154","doi-asserted-by":"crossref","unstructured":"Khalid S, Khalil T, Nasreen S (2014) A survey of feature selection and feature extraction techniques in machine learning. In: Science and information conference (SAI), IEEE, pp 372\u2013378","DOI":"10.1109\/SAI.2014.6918213"},{"key":"10745_CR155","unstructured":"Khan Z, Mumtaz W, Mumtaz AS, et\u00a0al (2022) Multiclass-classification of algae using dc-gan and transfer learning. In: International conference on image processing and robotics (ICIPRob), IEEE, pp 1\u20136"},{"issue":"5","key":"10745_CR156","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1109\/34.55109","volume":"12","author":"A Khotanzad","year":"1990","unstructured":"Khotanzad A, Hong YH (1990) Invariant image recognition by zernike moments. IEEE Trans Patt Anal Mach Intell (PAMI) 12(5):489\u2013497","journal-title":"IEEE Trans Patt Anal Mach Intell (PAMI)"},{"key":"10745_CR157","doi-asserted-by":"publisher","unstructured":"Kiko R, Simon-Martin S (2020) UVP5 data sorted with EcoTaxa and morphocluste https:\/\/doi.org\/10.17882\/73002","DOI":"10.17882\/73002"},{"issue":"5","key":"10745_CR158","doi-asserted-by":"publisher","first-page":"844","DOI":"10.1090\/S0002-9904-1975-13853-5","volume":"81","author":"J Kingman","year":"1975","unstructured":"Kingman J, Matheron G (1975) Random sets and integral geometry. Bull Am Math Soci 81(5):844\u2013847","journal-title":"Bull Am Math Soci"},{"issue":"1","key":"10745_CR159","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2105-15-218","volume":"15","author":"M Kloster","year":"2014","unstructured":"Kloster M, Kauer G, Beszteri B (2014) Sherpa: an image segmentation and outline feature extraction tool for diatoms and other objects. BMC Bioinform 15(1):1\u201317","journal-title":"BMC Bioinform"},{"issue":"1","key":"10745_CR160","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-71165-w","volume":"10","author":"M Kloster","year":"2020","unstructured":"Kloster M, Langenk\u00e4mper D, Zurowietz M et al (2020) Deep learning-based diatom taxonomy on virtual slides. Sci Rep 10(1):1\u201313","journal-title":"Sci Rep"},{"key":"10745_CR161","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1016\/j.patcog.2017.12.021","volume":"77","author":"S Kosov","year":"2018","unstructured":"Kosov S, Shirahama K, Li C et al (2018) Environmental microorganism classification using conditional random fields and deep convolutional neural networks. Patt Recogn 77:248\u2013261","journal-title":"Patt Recogn"},{"issue":"2","key":"10745_CR162","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1007\/s004260000024","volume":"64","author":"P Kovesi","year":"2000","unstructured":"Kovesi P (2000) Phase congruency: a low-level image invariant. Psychol Res 64(2):136\u2013148","journal-title":"Psychol Res"},{"key":"10745_CR163","unstructured":"Kovesi P (2003) Phase congruency detects corners and edges. In: Australian pattern recognition society conference: DICTA"},{"key":"10745_CR164","doi-asserted-by":"crossref","unstructured":"Kraft K, Sepp\u00e4l\u00e4 J, H\u00e4llfors H, et\u00a0al (2021) First application of ifcb high-frequency imaging-in-flow cytometry to investigate bloom-forming filamentous cyanobacteria in the baltic sea. Front Marine Sci, p 282","DOI":"10.3389\/fmars.2021.594144"},{"key":"10745_CR165","doi-asserted-by":"publisher","unstructured":"Kraft K, Haraguchi L, Velhonoja O, et\u00a0al (2022a) SYKE-phytoplankton_IFCB_Ut\u00f6_2021. https:\/\/doi.org\/10.23728\/b2share.7c273b6f409c47e98a868d6517be3ae3","DOI":"10.23728\/b2share.7c273b6f409c47e98a868d6517be3ae3"},{"key":"10745_CR166","doi-asserted-by":"publisher","first-page":"867695","DOI":"10.3389\/fmars.2022.867695","volume":"9","author":"K Kraft","year":"2022","unstructured":"Kraft K, Velhonoja O, Eerola T et al (2022) Towards operational phytoplankton recognition with automated high-throughput imaging, near-real-time data processing, and convolutional neural networks. Front Mar Sci 9:867695","journal-title":"Front Mar Sci"},{"key":"10745_CR167","doi-asserted-by":"publisher","unstructured":"Kraft K, Velhonoja O, Sepp\u00e4l\u00e4 J, et\u00a0al (2022c) SYKE-phytoplankton_IFCB_2022. https:\/\/doi.org\/10.23728\/b2share.abf913e5a6ad47e6baa273ae0ed6617a","DOI":"10.23728\/b2share.abf913e5a6ad47e6baa273ae0ed6617a"},{"key":"10745_CR168","unstructured":"Kramer KA (2005) Identifying Plankton from Grayscale Silhouette Images. Master\u2019s thesis, University of South Florida"},{"key":"10745_CR169","unstructured":"Kramer KA (2010) System for identifying plankton from the sipper instrument platform. University of South Florida"},{"key":"10745_CR170","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Neural Information Processing Systems (NIPS), pp 1097\u20131105"},{"key":"10745_CR171","unstructured":"Kuang Y (2015) Deep neural network for deep sea plankton classification. Project Report, Course CS231n: Convolutional Neural Networks for Visual Recognition, Stanford University, USA, https:\/\/pdfs.semanticscholar.org\/40fd\/606b61e15c28a509a5335b8cf6ffdefc 51bc.pdf"},{"issue":"3","key":"10745_CR172","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1016\/0146-664X(82)90034-X","volume":"18","author":"FP Kuhl","year":"1982","unstructured":"Kuhl FP, Giardina CR (1982) Elliptic fourier features of a closed contour. Comput Graphics Image Process 18(3):236\u2013258","journal-title":"Comput Graphics Image Process"},{"key":"10745_CR173","doi-asserted-by":"crossref","unstructured":"Kyathanahally S, Hardeman T, Merz E, et\u00a0al (2021a) Data for: Deep learning classification of lake zooplankton. https:\/\/opendata.eawag.ch\/dataset\/deep-learning-classification-of-zooplankton-from-lakes","DOI":"10.1101\/2021.08.12.455943"},{"key":"10745_CR174","doi-asserted-by":"crossref","unstructured":"Kyathanahally SP, Hardeman T, Merz E, et\u00a0al (2021b) Deep learning classification of lake zooplankton. Front Microbiol, p 3226","DOI":"10.1101\/2021.08.12.455943"},{"issue":"1","key":"10745_CR175","doi-asserted-by":"publisher","first-page":"18590","DOI":"10.1038\/s41598-022-21910-0","volume":"12","author":"SP Kyathanahally","year":"2022","unstructured":"Kyathanahally SP, Hardeman T, Reyes M et al (2022) Ensembles of data-efficient vision transformers as a new paradigm for automated classification in ecology. Sci Rep 12(1):18590","journal-title":"Sci Rep"},{"issue":"25","key":"10745_CR176","doi-asserted-by":"publisher","first-page":"28170","DOI":"10.1364\/OE.24.028170","volume":"24","author":"QT Lai","year":"2016","unstructured":"Lai QT, Lee KC, Tang AH et al (2016) High-throughput time-stretch imaging flow cytometry for multi-class classification of phytoplankton. Opt Expr 24(25):28170\u201328184","journal-title":"Opt Expr"},{"key":"10745_CR177","doi-asserted-by":"crossref","unstructured":"Lang K, Shan S, Lv W, et\u00a0al (2022) Image fusion method for improving the accuracy of ocean plankton recognition. In: OCEANS 2022-Chennai, IEEE, pp 1\u20134","DOI":"10.1109\/OCEANSChennai45887.2022.9775462"},{"issue":"3","key":"10745_CR178","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1007\/s11099-016-0663-2","volume":"55","author":"M Lauffer","year":"2017","unstructured":"Lauffer M, Genty F, Margueron S et al (2017) Morphological recognition with the addition of multi-band fluorescence excitation of chlorophylls of phytoplankton. Photosynthetica 55(3):434\u2013442","journal-title":"Photosynthetica"},{"key":"10745_CR179","doi-asserted-by":"publisher","first-page":"869088","DOI":"10.3389\/fmars.2022.869088","volume":"9","author":"KT Le","year":"2022","unstructured":"Le KT, Yuan Z, Syed A et al (2022) Benchmarking and Automating the Image Recognition Capability of an In Situ Plankton Imaging System. Front Mar Sci 9:869088","journal-title":"Front Mar Sci"},{"key":"10745_CR180","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436\u2013444","journal-title":"Nature"},{"key":"10745_CR181","doi-asserted-by":"crossref","unstructured":"Lee H, Park M, Kim J (2016) Plankton classification on imbalanced large scale database via convolutional neural networks with transfer learning. In: International Conference on Image Processing (ICIP), IEEE, pp 3713\u20133717","DOI":"10.1109\/ICIP.2016.7533053"},{"issue":"2","key":"10745_CR182","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1109\/PROC.1970.7593","volume":"58","author":"GG Lendaris","year":"1970","unstructured":"Lendaris GG, Stanley GL (1970) Diffraction-pattern sampling for automatic pattern recognition. Proc IEEE 58(2):198\u2013216","journal-title":"Proc IEEE"},{"issue":"4","key":"10745_CR183","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1007\/s10462-017-9572-4","volume":"51","author":"C Li","year":"2019","unstructured":"Li C, Wang K, Xu N (2019) A survey for the applications of content-based microscopic image analysis in microorganism classification domains. Artif Intell Rev 51(4):577\u2013646","journal-title":"Artif Intell Rev"},{"issue":"1","key":"10745_CR184","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1109\/JOE.2021.3106122","volume":"47","author":"J Li","year":"2021","unstructured":"Li J, Chen T, Yang Z et al (2021) Development of a buoy-borne underwater imaging system for in situ mesoplankton monitoring of coastal waters. IEEE J Oceanic Eng 47(1):88\u2013110","journal-title":"IEEE J Oceanic Eng"},{"key":"10745_CR185","doi-asserted-by":"publisher","unstructured":"Li J, Yang Z, Chen T (2021b) DYB-PlanktonNet, https:\/\/doi.org\/10.21227\/875n-f104","DOI":"10.21227\/875n-f104"},{"key":"10745_CR186","doi-asserted-by":"crossref","unstructured":"Li P, Xie J, Wang Q, et\u00a0al (2017) Is second-order information helpful for large-scale visual recognition? In: International conference on computer vision (ICCV), pp 2070\u20132078","DOI":"10.1109\/ICCV.2017.228"},{"issue":"4","key":"10745_CR187","doi-asserted-by":"publisher","first-page":"1427","DOI":"10.1093\/icesjms\/fsz171","volume":"77","author":"Q Li","year":"2019","unstructured":"Li Q, Sun X, Dong J et al (2019) Developing a microscopic image dataset in support of intelligent phytoplankton detection using deep learning. ICES J Mar Sci 77(4):1427\u20131439","journal-title":"ICES J Mar Sci"},{"key":"10745_CR188","unstructured":"Li X, Cui Z (2016) Deep residual networks for plankton classification. In: OCEANS conference, pp 1\u20134"},{"key":"10745_CR189","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/6536925","author":"X Li","year":"2019","unstructured":"Li X, Long R, Yan J et al (2019) Tanet: a tiny plankton classification network for mobile devices. Mobile Inform Syst. https:\/\/doi.org\/10.1155\/2019\/6536925","journal-title":"Mobile Inform Syst"},{"issue":"6","key":"10745_CR190","doi-asserted-by":"publisher","first-page":"636","DOI":"10.3390\/jmse9060636","volume":"9","author":"Y Li","year":"2021","unstructured":"Li Y, Guo J, Guo X et al (2021) Plankton detection with adversarial learning and a densely connected deep learning model for class imbalanced distribution. J Marine Sci Eng 9(6):636","journal-title":"J Marine Sci Eng"},{"key":"10745_CR191","doi-asserted-by":"publisher","first-page":"102783","DOI":"10.1016\/j.apor.2021.102783","volume":"114","author":"Y Li","year":"2021","unstructured":"Li Y, Guo J, Guo X et al (2021) Toward in situ zooplankton detection with a densely connected yolov3 model. Appl Ocean Res 114:102783","journal-title":"Appl Ocean Res"},{"issue":"4","key":"10745_CR192","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1109\/JOE.2013.2280035","volume":"39","author":"Z Li","year":"2014","unstructured":"Li Z, Zhao F, Liu J et al (2014) Pairwise nonparametric discriminant analysis for binary plankton image recognition. IEEE J Oceanic Eng 39(4):695\u2013701","journal-title":"IEEE J Oceanic Eng"},{"key":"10745_CR193","doi-asserted-by":"crossref","unstructured":"Libreros J, Bueno G, Trujillo M, et\u00a0al (2018) Automated identification and classification of diatoms from water resources. In: Iberoamerican Congress on Pattern Recognition (CIARP), Springer, pp 496\u2013503","DOI":"10.1007\/978-3-030-13469-3_58"},{"key":"10745_CR194","doi-asserted-by":"crossref","unstructured":"Lim B, Son S, Kim H, et\u00a0al (2017) Enhanced deep residual networks for single image super-resolution. In: Conference on computer vision and pattern recognition (CVPR) Workshops, pp 136\u2013144","DOI":"10.1109\/CVPRW.2017.151"},{"issue":"2","key":"10745_CR195","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1109\/TPAMI.2007.41","volume":"29","author":"H Ling","year":"2007","unstructured":"Ling H, Jacobs DW (2007) Shape classification using the inner-distance. IEEE Trans Patt Anal Mach Intell (PAMI) 29(2):286\u2013299","journal-title":"IEEE Trans Patt Anal Mach Intell (PAMI)"},{"key":"10745_CR196","unstructured":"Lisin DA (2006) Image classification with bags of local features. University of Massachusetts Amherst"},{"key":"10745_CR197","doi-asserted-by":"crossref","unstructured":"Lisin DA, Mattar MA, Blaschko MB, et\u00a0al (2005) Combining local and global image features for object class recognition. In: Conference on computer vision and pattern recognition (CVPR) workshops, IEEE, pp 47","DOI":"10.1109\/CVPR.2005.433"},{"key":"10745_CR198","doi-asserted-by":"crossref","unstructured":"Liu J, Du A, Wang C, et\u00a0al (2018a) Deep pyramidal residual networks for plankton image classification. In: OCEANS Techno-Oceans (OTO), IEEE, pp 1\u20135","DOI":"10.1109\/OCEANSKOBE.2018.8559106"},{"key":"10745_CR199","doi-asserted-by":"crossref","unstructured":"Liu J, Du A, Wang C, et\u00a0al (2018b) Teaching squeeze-and-excitation pyramidnet for imbalanced image classification with gan-based curriculum learning. In: International conference on pattern recognition (ICPR), IEEE, pp 2444\u20132449","DOI":"10.1109\/ICPR.2018.8546037"},{"key":"10745_CR200","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, et\u00a0al (2016) SSD: Single shot multibox detector. In: European conference on computer vision (ECCV), pp 21\u201337","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"10745_CR201","doi-asserted-by":"crossref","unstructured":"Liu Y, Qiao X, Gao R (2021) Plankton classification on imbalanced dataset via hybrid resample method with lightbgm. International conference on image, vision and computing (ICIVC), IEEE, pp 191\u2013195","DOI":"10.1109\/ICIVC52351.2021.9526988"},{"key":"10745_CR202","doi-asserted-by":"crossref","unstructured":"Liu Z, Watson J (2020) Shape-based image classification and identification system for digital holograms of marine particles and plankton. In: Global Oceans 2020: Singapore\u2013U.S. Gulf Coast, pp 1\u20135","DOI":"10.1109\/IEEECONF38699.2020.9389156"},{"key":"10745_CR203","doi-asserted-by":"crossref","unstructured":"Liu Z, Watson J, Allen A (2017) Efficient affine-invariant fourier descriptors for identification of marine plankton. In: OCEANS 2017-Aberdeen, IEEE, pp 1\u20139","DOI":"10.1109\/OCEANSE.2017.8084832"},{"key":"10745_CR204","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, et\u00a0al (2021b) Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 10012\u201310022","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"10745_CR205","doi-asserted-by":"crossref","unstructured":"Liu Z, Mao H, Wu CY, et\u00a0al (2022) A convnet for the 2020s. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 11976\u201311986","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"10745_CR206","doi-asserted-by":"publisher","first-page":"196","DOI":"10.3389\/fmars.2019.00196","volume":"6","author":"F Lombard","year":"2019","unstructured":"Lombard F, Boss E, Waite AM et al (2019) Globally consistent quantitative observations of planktonic ecosystems. Front Mar Sci 6:196","journal-title":"Front Mar Sci"},{"key":"10745_CR207","doi-asserted-by":"crossref","unstructured":"Lowe DG (1999) Object recognition from local scale-invariant features. In: International conference on computer vision (ICCV), IEEE, pp 1150\u20131157","DOI":"10.1109\/ICCV.1999.790410"},{"issue":"2","key":"10745_CR208","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"DG Lowe","year":"2004","unstructured":"Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comp Vision (IJCV) 60(2):91\u2013110","journal-title":"Int J Comp Vision (IJCV)"},{"key":"10745_CR209","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.ecoinf.2019.02.007","volume":"51","author":"A Lumini","year":"2019","unstructured":"Lumini A, Nanni L (2019) Deep learning and transfer learning features for plankton classification. Eco Inform 51:33\u201343","journal-title":"Eco Inform"},{"key":"10745_CR210","doi-asserted-by":"crossref","unstructured":"Lumini A, Nanni L (2019b) Ocean ecosystems plankton classification. In: Recent advances in computer vision. Springer, pp 261\u2013280","DOI":"10.1007\/978-3-030-03000-1_11"},{"issue":"3\/4","key":"10745_CR211","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1016\/j.aci.2019.11.004","volume":"19","author":"A Lumini","year":"2020","unstructured":"Lumini A, Nanni L, Maguolo G (2020) Deep learning for plankton and coral classification. Appl Comp Inform 19(3\/4):265\u201383","journal-title":"Appl Comp Inform"},{"key":"10745_CR212","doi-asserted-by":"publisher","first-page":"814","DOI":"10.1002\/lom3.10285","volume":"16","author":"JY Luo","year":"2018","unstructured":"Luo JY, Irisson JO, Graham B et al (2018) Automated plankton image analysis using convolutional neural networks. Limnol Oceanogr Methods 16:814\u2013827","journal-title":"Limnol Oceanogr Methods"},{"issue":"3","key":"10745_CR213","doi-asserted-by":"publisher","first-page":"428","DOI":"10.4304\/jsw.6.3.428-435","volume":"6","author":"Q Luo","year":"2011","unstructured":"Luo Q, Gao Y, Luo J et al (2011) Automatic identification of diatoms with circular shape using texture analysis. J Software 6(3):428\u2013435","journal-title":"J Software"},{"issue":"11","key":"10745_CR214","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1002\/cyto.a.24321","volume":"99","author":"S Luo","year":"2021","unstructured":"Luo S, Nguyen KT, Nguyen BT et al (2021) Deep learning-enabled imaging flow cytometry for high-speed cryptosporidium and giardia detection. Cytometry A 99(11):1123\u20131133","journal-title":"Cytometry A"},{"issue":"11","key":"10745_CR215","doi-asserted-by":"publisher","first-page":"2100073","DOI":"10.1002\/aisy.202100073","volume":"3","author":"S Luo","year":"2021","unstructured":"Luo S, Shi Y, Chin LK et al (2021) Machine-learning-assisted intelligent imaging flow cytometry: a review. Adv Intell Syst 3(11):2100073","journal-title":"Adv Intell Syst"},{"key":"10745_CR216","unstructured":"Luo T (2005) Scaling up support vector machines with application to plankton recognition. PhD thesis, University of South Florida"},{"key":"10745_CR217","unstructured":"Luo T, Kramer K, Goldgof D et al (2003) Learning to recognize plankton. International conference on systems, man and cybernetics, IEEE, pp 888\u2013893"},{"issue":"4","key":"10745_CR218","doi-asserted-by":"publisher","first-page":"1753","DOI":"10.1109\/TSMCB.2004.830340","volume":"34","author":"T Luo","year":"2004","unstructured":"Luo T, Kramer K, Goldgof DB et al (2004) Recognizing plankton images from the shadow image particle profiling evaluation recorder. IEEE Trans Syst, Man, Cybernet Part B (Cybernet) 34(4):1753\u20131762","journal-title":"IEEE Trans Syst, Man, Cybernet Part B (Cybernet)"},{"issue":"Apr","key":"10745_CR219","first-page":"589","volume":"6","author":"T Luo","year":"2005","unstructured":"Luo T, Kramer K, Goldgof DB et al (2005) Active learning to recognize multiple types of plankton. J Mach Learn Res 6(Apr):589\u2013613","journal-title":"J Mach Learn Res"},{"key":"10745_CR220","doi-asserted-by":"crossref","unstructured":"Ma N, Zhang X, Zheng HT, et\u00a0al (2018) Shufflenet v2: Practical guidelines for efficient cnn architecture design. In: Proceedings of the European conference on computer vision (ECCV), pp 116\u2013131","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"10745_CR221","doi-asserted-by":"crossref","unstructured":"Ma W, Chen T, Zhang Z, et\u00a0al (2021) Super-resolution for in situ plankton images. In: International conference on computer vision (ICCV), pp 3683\u20133692","DOI":"10.1109\/ICCVW54120.2021.00411"},{"issue":"7312","key":"10745_CR222","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1038\/467154a","volume":"467","author":"N MacLeod","year":"2010","unstructured":"MacLeod N, Benfield M, Culverhouse P (2010) Time to automate identification. Nature 467(7312):154\u2013155","journal-title":"Nature"},{"issue":"1","key":"10745_CR223","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12862-020-01734-0","volume":"21","author":"L MacNeil","year":"2021","unstructured":"MacNeil L, Missan S, Luo J et al (2021) Plankton classification with high-throughput submersible holographic microscopy and transfer learning. BMC Ecol Evol 21(1):1\u201311","journal-title":"BMC Ecol Evol"},{"issue":"1","key":"10745_CR224","doi-asserted-by":"publisher","first-page":"10443","DOI":"10.1038\/s41598-023-37627-7","volume":"13","author":"A Maracani","year":"2023","unstructured":"Maracani A, Pastore VP, Natale L et al (2023) In-domain versus out-of-domain transfer learning in plankton image classification. Sci Rep 13(1):10443","journal-title":"Sci Rep"},{"key":"10745_CR225","doi-asserted-by":"crossref","unstructured":"Mechrez R, Talmi I, Zelnik-Manor L (2018) The contextual loss for image transformation with non-aligned data. In: European conference on computer vision (ECCV), pp 768\u2013783","DOI":"10.1007\/978-3-030-01264-9_47"},{"issue":"1","key":"10745_CR226","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-88661-2","volume":"11","author":"Y Mirasbekov","year":"2021","unstructured":"Mirasbekov Y, Zhumakhanova A, Zhantuyakova A et al (2021) Semi-automated classification of colonial microcystis by flowcam imaging flow cytometry in mesocosm experiment reveals high heterogeneity during seasonal bloom. Sci Rep 11(1):1\u201314","journal-title":"Sci Rep"},{"issue":"4","key":"10745_CR227","doi-asserted-by":"publisher","first-page":"e12972","DOI":"10.1111\/jeu.12972","volume":"70","author":"A Mitra","year":"2023","unstructured":"Mitra A, Caron DA, Faure E et al (2023) The mixoplankton database (mdb): Diversity of photo-phago-trophic plankton in form, function, and distribution across the global ocean. J Eukary Microbiol 70(4):e12972","journal-title":"J Eukary Microbiol"},{"key":"10745_CR228","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.marmicro.2019.01.005","volume":"147","author":"R Mitra","year":"2019","unstructured":"Mitra R, Marchitto T, Ge Q et al (2019) Automated species-level identification of planktic foraminifera using convolutional neural networks, with comparison to human performance. Mar Micropaleontol 147:16\u201324","journal-title":"Mar Micropaleontol"},{"key":"10745_CR229","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3143887","author":"S Mittal","year":"2022","unstructured":"Mittal S, Srivastava S, Jayanth JP (2022) A survey of deep learning techniques for underwater image classification. IEEE Trans Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2022.3143887","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"10745_CR230","doi-asserted-by":"crossref","unstructured":"Moniruzzaman M, Islam SMS, Bennamoun M, et\u00a0al (2017) Deep learning on underwater marine object detection: A survey. In: International conference on advanced concepts for intelligent vision systems (ACIVS), Springer, pp 150\u2013160","DOI":"10.1007\/978-3-319-70353-4_13"},{"issue":"Suppl17","key":"10745_CR231","doi-asserted-by":"publisher","first-page":"S25","DOI":"10.1186\/1471-2105-13-S17-S25","volume":"13","author":"MA Mosleh","year":"2012","unstructured":"Mosleh MA, Manssor H, Malek S et al (2012) A preliminary study on automated freshwater algae recognition and classification system. BMC Bioinform 13(Suppl17):S25","journal-title":"BMC Bioinform"},{"key":"10745_CR232","doi-asserted-by":"crossref","unstructured":"Movshovitz-Attias Y, Toshev A, Leung TK, et\u00a0al (2017) No fuss distance metric learning using proxies. In: International conference on computer vision (ICCV), pp 360\u2013368","DOI":"10.1109\/ICCV.2017.47"},{"key":"10745_CR233","doi-asserted-by":"crossref","unstructured":"Nandini TS, Swethaa S, Bolem S, et\u00a0al (2022) Real-time classification of plankton species using convolutional neural networks. In: OCEANS 2022-Chennai, IEEE, pp 1\u20135","DOI":"10.1109\/OCEANSChennai45887.2022.9775280"},{"issue":"1","key":"10745_CR234","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1002\/lno.10618","volume":"63","author":"AR Nayak","year":"2018","unstructured":"Nayak AR, McFarland MN, Sullivan JM et al (2018) Evidence for ubiquitous preferential particle orientation in representative oceanic shear flows. Limnol Oceanogr 63(1):122\u2013143","journal-title":"Limnol Oceanogr"},{"key":"10745_CR235","doi-asserted-by":"crossref","unstructured":"Nepovinnykh E, Eerola T, Kalviainen H (2020) Siamese network based pelage pattern matching for ringed seal re-identification. In: Winter conference on applications of computer vision (WACV) workshops, pp 25\u201334","DOI":"10.1109\/WACVW50321.2020.9096935"},{"issue":"7","key":"10745_CR236","doi-asserted-by":"publisher","first-page":"971","DOI":"10.1109\/TPAMI.2002.1017623","volume":"24","author":"T Ojala","year":"2002","unstructured":"Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Patt Anal Mach Intell (PAMI) 24(7):971\u2013987","journal-title":"IEEE Trans Patt Anal Mach Intell (PAMI)"},{"key":"10745_CR237","doi-asserted-by":"publisher","first-page":"195","DOI":"10.4319\/lom.2007.5.195","volume":"5","author":"RJ Olson","year":"2007","unstructured":"Olson RJ, Sosik HM (2007) A submersible imaging-in-flow instrument to analyze nano-and microplankton: Imaging flowcytobot. Limnol Oceanogr Methods 5:195\u2013203","journal-title":"Limnol Oceanogr Methods"},{"key":"10745_CR238","doi-asserted-by":"crossref","unstructured":"Orenstein EC, Beijbom O (2017) Transfer learning and deep feature extraction for planktonic image data sets. In: Winter conference on applications of computer vision (WACV), IEEE, pp 1082\u20131088","DOI":"10.1109\/WACV.2017.125"},{"key":"10745_CR239","unstructured":"Orenstein EC, Beijbom O, Peacock EE, et\u00a0al (2015) WHOI-plankton-a large scale fine grained visual recognition benchmark dataset for plankton classification. arXiv preprint arXiv:1510.00745"},{"issue":"12","key":"10745_CR240","doi-asserted-by":"publisher","first-page":"739","DOI":"10.1002\/lom3.10399","volume":"18","author":"EC Orenstein","year":"2020","unstructured":"Orenstein EC, Kenitz KM, Roberts PL et al (2020) Semi-and fully supervised quantification techniques to improve population estimates from machine classifiers. Limnol Oceanogr Methods 18(12):739\u2013753","journal-title":"Limnol Oceanogr Methods"},{"issue":"11","key":"10745_CR241","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1002\/lom3.10394","volume":"18","author":"EC Orenstein","year":"2020","unstructured":"Orenstein EC, Ratelle D, Brise\u00f1o-Avena C et al (2020) The scripps plankton camera system: a framework and platform for in situ microscopy. Limnol Oceanogr Methods 18(11):681\u2013695","journal-title":"Limnol Oceanogr Methods"},{"key":"10745_CR242","first-page":"23","volume":"11","author":"N Otsu","year":"1975","unstructured":"Otsu N (1975) A threshold selection method from gray-level histograms. Automatica 11:23\u201327","journal-title":"Automatica"},{"issue":"10","key":"10745_CR243","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2009","unstructured":"Pan SJ, Yang Q (2009) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345\u20131359","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"10745_CR244","doi-asserted-by":"crossref","unstructured":"Pardeshi R, Deshmukh PD (2019) Classification of microscopic algae: An observational study with alexnet. In: International conference on soft computing and signal processing (ICSCSP), Springer, pp 309\u2013316","DOI":"10.1007\/978-981-15-2475-2_29"},{"issue":"1","key":"10745_CR245","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-68662-3","volume":"10","author":"VP Pastore","year":"2020","unstructured":"Pastore VP, Zimmerman TG, Biswas SK et al (2020) Annotation-free learning of plankton for classification and anomaly detection. Sci Rep 10(1):1\u201315","journal-title":"Sci Rep"},{"key":"10745_CR246","doi-asserted-by":"crossref","unstructured":"Pastore VP, Megiddo N, Bianco S (2022) An anomaly detection approach for plankton species discovery. In: International conference on image analysis and processing, Springer, pp 599\u2013609","DOI":"10.1007\/978-3-031-06430-2_50"},{"key":"10745_CR247","doi-asserted-by":"publisher","first-page":"104764","DOI":"10.1016\/j.imavis.2023.104764","volume":"137","author":"VP Pastore","year":"2023","unstructured":"Pastore VP, Ciranni M, Bianco S et al (2023) Efficient unsupervised learning of biological images with compressed deep features. Image Vis Comput 137:104764","journal-title":"Image Vis Comput"},{"key":"10745_CR248","doi-asserted-by":"publisher","first-page":"460","DOI":"10.3390\/app7050460","volume":"7","author":"A Pedraza","year":"2017","unstructured":"Pedraza A, Bueno G, Deniz O et al (2017) Automated diatom classification (Part B): A deep learning approach. Appl Sci 7:460","journal-title":"Appl Sci"},{"key":"10745_CR249","doi-asserted-by":"crossref","unstructured":"Pedraza A, Bueno G, Deniz O, et\u00a0al (2018) Lights and pitfalls of convolutional neural networks for diatom identification. In: Optics, photonics, and digital technologies for imaging applications V, international society for optics and photonics (SPIE), p 106790G","DOI":"10.1117\/12.2309488"},{"issue":"9","key":"10745_CR250","doi-asserted-by":"publisher","first-page":"462","DOI":"10.4319\/lom.2010.8.462","volume":"8","author":"M Picheral","year":"2010","unstructured":"Picheral M, Guidi L, Stemmann L et al (2010) The underwater vision profiler 5: An advanced instrument for high spatial resolution studies of particle size spectra and zooplankton. Limnol Oceanogr Methods 8(9):462\u2013473","journal-title":"Limnol Oceanogr Methods"},{"key":"10745_CR251","unstructured":"Picheral M, Colin S, Irisson JO (2017) EcoTaxa, a tool for the taxonomic classification of images. https:\/\/ecotaxa.obs-vlfr.fr\/"},{"issue":"3","key":"10745_CR252","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1002\/lom3.10413","volume":"19","author":"RM Plonus","year":"2021","unstructured":"Plonus RM, Conradt J, Harmer A et al (2021) Automatic plankton image classification -Can capsules and filters help cope with data set shift? Limnol Oceanogr Methods 19(3):176\u2013195","journal-title":"Limnol Oceanogr Methods"},{"key":"10745_CR253","doi-asserted-by":"publisher","unstructured":"Plonus RM, Conradt J, Harmer A, et\u00a0al (2021b) Automatic plankton image classification \u2013 can capsules and filters help coping with data set shift? (Dataset) https:\/\/doi.org\/10.5281\/zenodo.4431509","DOI":"10.5281\/zenodo.4431509"},{"key":"10745_CR254","first-page":"535","volume-title":"Image feature extraction","author":"WK Pratt","year":"2007","unstructured":"Pratt WK (2007) Image feature extraction, vol 16. Wiley, Hoboken, pp 535\u2013577"},{"key":"10745_CR255","doi-asserted-by":"crossref","unstructured":"Pu Y, Feng Z, Wang Z, et\u00a0al (2021) Anomaly detection for in situ marine plankton images. In: International conference on computer vision (ICCV), pp 3661\u20133671","DOI":"10.1109\/ICCVW54120.2021.00409"},{"key":"10745_CR256","doi-asserted-by":"crossref","unstructured":"Py O, Hong H, Zhongzhi S (2016) Plankton classification with deep convolutional neural networks. In: Information technology, networking, electronic and automation control conference (ITNEC), IEEE, pp 132\u2013136","DOI":"10.1109\/ITNEC.2016.7560334"},{"key":"10745_CR257","doi-asserted-by":"crossref","unstructured":"Qi H, Brown M, Lowe DG (2018) Low-shot learning with imprinted weights. In: Conference on computer vision and pattern recognition (CVPR), pp 5822\u20135830","DOI":"10.1109\/CVPR.2018.00610"},{"key":"10745_CR258","doi-asserted-by":"crossref","unstructured":"Qiao X, Tang M, Tang Z, et\u00a0al (2021) Classification of phytoplankton digital holograms using transfer learning. In: Symposium on novel photoelectronic detection technology and applications, SPIE, pp 1721\u20131726","DOI":"10.1117\/12.2587333"},{"key":"10745_CR259","doi-asserted-by":"crossref","unstructured":"Rachman A, Suwarno AS, Nurdjaman S (2022) Application of deep (machine) learning for phytoplankton identification using microscopy images. In: International conference on biological science (ICBS), Atlantis Press, pp 213\u2013224","DOI":"10.2991\/absr.k.220406.032"},{"issue":"7","key":"10745_CR260","doi-asserted-by":"publisher","first-page":"1655","DOI":"10.1109\/TPAMI.2018.2846566","volume":"41","author":"F Radenovi\u0107","year":"2018","unstructured":"Radenovi\u0107 F, Tolias G, Chum O (2018) Fine-tuning cnn image retrieval with no human annotation. IEEE Trans Patt Anal Mach Intell (PAMI) 41(7):1655\u20131668","journal-title":"IEEE Trans Patt Anal Mach Intell (PAMI)"},{"key":"10745_CR261","unstructured":"Radford A, Metz L, Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434"},{"key":"10745_CR262","doi-asserted-by":"crossref","unstructured":"Raitoharju J, Riabchenko E, Meissner K, et\u00a0al (2016) Data enrichment in fine-grained classification of aquatic macroinvertebrates. In: Workshop on computer vision for analysis of underwater imagery (CVAUI), IEEE, pp 43\u201348","DOI":"10.1109\/CVAUI.2016.020"},{"issue":"3","key":"10745_CR263","doi-asserted-by":"publisher","first-page":"1801","DOI":"10.1007\/s11831-021-09639-x","volume":"9","author":"P Rani","year":"2021","unstructured":"Rani P, Kotwal S, Manhas J et al (2021) Machine learning and deep learning based computational approaches in automatic microorganisms image recognition: methodologies, challenges, and developments. Arch Computat Meth Eng 9(3):1801\u20131837","journal-title":"Arch Computat Meth Eng"},{"key":"10745_CR264","unstructured":"Ravela SS (2003) On multi-scale differential features and their representations for image retrieval and recognition. University of Massachusetts Amherst"},{"key":"10745_CR265","doi-asserted-by":"crossref","unstructured":"Rawat SS, Bisht A, Nijhawan R (2019) A deep learning based cnn framework approach for plankton classification. In: International Conference on Image Information Processing (ICIIP), IEEE, pp 268\u2013273","DOI":"10.1109\/ICIIP47207.2019.8985838"},{"key":"10745_CR266","unstructured":"Redmon J, Farhadi A (2018) YOLOv3: an incremental improvement. arXiv preprint arXiv:1804.02767"},{"key":"10745_CR267","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R, et\u00a0al (2016) You only look once: unified, real-time object detection. In: Conference on computer vision and pattern recognition (CVPR), pp 779\u2013788","DOI":"10.1109\/CVPR.2016.91"},{"issue":"8","key":"10745_CR268","doi-asserted-by":"publisher","first-page":"830","DOI":"10.1109\/34.85675","volume":"13","author":"TH Reiss","year":"1991","unstructured":"Reiss TH (1991) The revised fundamental theorem of moment invariants. IEEE Trans Patt Anal Mach Intell (PAMI) 13(8):830\u2013834","journal-title":"IEEE Trans Patt Anal Mach Intell (PAMI)"},{"issue":"6","key":"10745_CR269","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren S, He K, Girshick R et al (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Patt Anal Mach Intell (PAMI) 39(6):1137\u20131149","journal-title":"IEEE Trans Patt Anal Mach Intell (PAMI)"},{"key":"10745_CR270","doi-asserted-by":"publisher","first-page":"105923","DOI":"10.1016\/j.cmpb.2020.105923","volume":"200","author":"D Rivas-Villar","year":"2021","unstructured":"Rivas-Villar D, Rouco J, Carballeira R et al (2021) Fully automatic detection and classification of phytoplankton specimens in digital microscopy images. Comput Methods Programs Biomed 200:105923","journal-title":"Comput Methods Programs Biomed"},{"key":"10745_CR271","doi-asserted-by":"crossref","unstructured":"Rivas-Villar D, Morano J, Rouco J, et\u00a0al (2022) Deep features-based approaches for phytoplankton classification in microscopy images. In: International conference on computer aided systems theory, Springer, pp 419\u2013426","DOI":"10.1007\/978-3-031-25312-6_49"},{"issue":"9","key":"10745_CR272","doi-asserted-by":"publisher","first-page":"708","DOI":"10.1002\/jemt.20338","volume":"69","author":"K Rodenacker","year":"2006","unstructured":"Rodenacker K, Hense B, J\u00fctting U et al (2006) Automatic analysis of aqueous specimens for phytoplankton structure recognition and population estimation. Microsc Res Tech 69(9):708\u2013720","journal-title":"Microsc Res Tech"},{"key":"10745_CR273","doi-asserted-by":"crossref","unstructured":"Rodrigues FCM, Hirata NS, Abello AA, et\u00a0al (2018) Evaluation of transfer learning scenarios in plankton image classification. In: International joint conference on computer vision, imaging and computer graphics theory and applications (VISIGRAPP), pp 359\u2013366","DOI":"10.5220\/0006626703590366"},{"key":"10745_CR274","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/bs.amb.2022.09.002","volume":"93","author":"AD Rogers","year":"2022","unstructured":"Rogers AD, Appeltans W, Assis J et al (2022) Chapter two - discovering marine biodiversity in the 21st century. Adv Mar Biol 93:23\u2013115","journal-title":"Adv Mar Biol"},{"key":"10745_CR275","doi-asserted-by":"publisher","first-page":"103271","DOI":"10.1016\/j.engappai.2019.103271","volume":"87","author":"J Ruiz-Santaquiteria","year":"2020","unstructured":"Ruiz-Santaquiteria J, Bueno G, Deniz O et al (2020) Semantic versus instance segmentation in microscopic algae detection. Eng Appl Artif Intell 87:103271","journal-title":"Eng Appl Artif Intell"},{"key":"10745_CR276","unstructured":"Salvesen E (2021) Unsupervised methods for in-situ classification of plankton taxa. Master\u2019s thesis, NTNU"},{"key":"10745_CR277","doi-asserted-by":"crossref","unstructured":"Salvesen E, Saad A, Stahl A (2020) Robust methods of unsupervised clustering to discover new planktonic species in-situ. In: Global Oceans 2020: Singapore\u2013US Gulf Coast, IEEE, pp 1\u20139","DOI":"10.1109\/IEEECONF38699.2020.9389188"},{"key":"10745_CR278","doi-asserted-by":"crossref","unstructured":"Salvesen E, Saad A, Stahl A (2022) Robust deep unsupervised learning framework to discover unseen plankton species. In: Fourteenth international conference on machine vision, SPIE, pp 241\u2013250","DOI":"10.1117\/12.2622489"},{"key":"10745_CR279","doi-asserted-by":"publisher","first-page":"e6770","DOI":"10.7717\/peerj.6770","volume":"7","author":"C S\u00e1nchez","year":"2019","unstructured":"S\u00e1nchez C, Crist\u00f3bal G, Bueno G (2019) Diatom identification including life cycle stages through morphological and texture descriptors. PeerJ 7:e6770","journal-title":"PeerJ"},{"key":"10745_CR280","doi-asserted-by":"crossref","unstructured":"S\u00e1nchez C, V\u00e1llez N, Bueno G, et\u00a0al (2019b) Diatom classification including morphological adaptations using cnns. In: Iberian conference on pattern recognition and image analysis (IbPRIA), Springer, pp 317\u2013328","DOI":"10.1007\/978-3-030-31332-6_28"},{"key":"10745_CR281","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, et\u00a0al (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In: Conference on computer vision and pattern recognition (CVPR), pp 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"issue":"3","key":"10745_CR282","first-page":"035007","volume":"4","author":"T Schanz","year":"2023","unstructured":"Schanz T, M\u00f6ller KO, R\u00fchl S et al (2023) Robust detection of marine life with label-free image feature learning and probability calibration. Mach Learn: Sci Technol 4(3):035007","journal-title":"Mach Learn: Sci Technol"},{"key":"10745_CR283","doi-asserted-by":"crossref","unstructured":"Scherrer R, Govan R, Quiniou T, et\u00a0al (2021) Automatic plankton detection and classification on raw hologram with a single deep learning architecture. In: International conference on computational intelligence methods for bioinformatics and biostatistics (CIBB)","DOI":"10.1007\/978-3-031-20837-9_3"},{"issue":"19","key":"10745_CR284","doi-asserted-by":"publisher","first-page":"6661","DOI":"10.3390\/s21196661","volume":"21","author":"L Schmarje","year":"2021","unstructured":"Schmarje L, Br\u00fcnger J, Santarossa M et al (2021) Fuzzy Overclustering: semi-supervised classification of fuzzy labels with overclustering and inverse cross-entropy. Sensors 21(19):6661","journal-title":"Sensors"},{"issue":"1","key":"10745_CR285","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1038\/s41597-022-01491-3","volume":"9","author":"T Schoening","year":"2022","unstructured":"Schoening T, Durden JM, Faber C et al (2022) Making marine image data FAIR. Scient Data 9(1):414","journal-title":"Scient Data"},{"issue":"7","key":"10745_CR286","doi-asserted-by":"publisher","first-page":"2775","DOI":"10.3390\/s22072775","volume":"22","author":"SM Schr\u00f6der","year":"2022","unstructured":"Schr\u00f6der SM, Kiko R (2022) Assessing representation learning and clustering algorithms for computer-assisted image annotation-simulating and benchmarking morphocluster. Sensors 22(7):2775","journal-title":"Sensors"},{"key":"10745_CR287","doi-asserted-by":"crossref","unstructured":"Schr\u00f6der SM, Kiko R, Irisson JO, et\u00a0al (2018) Low-shot learning of plankton categories. In: German conference on pattern recognition (GCPR), Springer, pp 391\u2013404","DOI":"10.1007\/978-3-030-12939-2_27"},{"issue":"11","key":"10745_CR288","doi-asserted-by":"publisher","first-page":"3060","DOI":"10.3390\/s20113060","volume":"20","author":"SM Schr\u00f6der","year":"2020","unstructured":"Schr\u00f6der SM, Kiko R, Koch R (2020) Morphocluster: efficient annotation of plankton images by clustering. Sensors 20(11):3060","journal-title":"Sensors"},{"key":"10745_CR289","doi-asserted-by":"publisher","DOI":"10.2971\/jeos.2010.10017s","author":"J Schulz","year":"2010","unstructured":"Schulz J, Barz K, Ayon P et al (2010) Imaging of plankton specimens with the lightframe on-sight keyspecies investigation (LOKI) system. J Eur Opt Soci. https:\/\/doi.org\/10.2971\/jeos.2010.10017s","journal-title":"J Eur Opt Soci"},{"issue":"1","key":"10745_CR290","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2105-14-115","volume":"14","author":"K Schulze","year":"2013","unstructured":"Schulze K, Tillich UM, Dandekar T et al (2013) Planktovision-an automated analysis system for the identification of phytoplankton. BMC Bioinform 14(1):1\u201310","journal-title":"BMC Bioinform"},{"key":"10745_CR291","doi-asserted-by":"crossref","unstructured":"Selvaraju RR, Cogswell M, Das A, et\u00a0al (2017) Grad-cam: Visual explanations from deep networks via gradient-based localization. In: International conference on computer vision (ICCV), pp 618\u2013626","DOI":"10.1109\/ICCV.2017.74"},{"key":"10745_CR292","unstructured":"Shan S, Zhang W, Wang X et al (2020) Automated red tide algae recognition by the color microscopic image. In: International congress on image and signal processing. BioMedical engineering and informatics (CISP-BMEI), IEEE, pp 852\u2013861"},{"issue":"5","key":"10745_CR293","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1109\/TNNLS.2014.2330900","volume":"26","author":"L Shao","year":"2014","unstructured":"Shao L, Zhu F, Li X (2014) Transfer learning for visual categorization: a survey. IEEE Trans Neural Netw Learn Syst 26(5):1019\u20131034","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"10745_CR294","doi-asserted-by":"publisher","first-page":"1174347","DOI":"10.3389\/fmars.2023.1174347","volume":"10","author":"G Si","year":"2023","unstructured":"Si G, Xiao Y, Wei B et al (2023) Token-selective vision transformer for fine-grained image recognition of marine organisms. Front Mar Sci 10:1174347","journal-title":"Front Mar Sci"},{"key":"10745_CR295","doi-asserted-by":"publisher","first-page":"285","DOI":"10.3354\/meps168285","volume":"168","author":"CK Sieracki","year":"1998","unstructured":"Sieracki CK, Sieracki ME, Yentsch CS (1998) An imaging-in-flow system for automated analysis of marine microplankton. Mar Ecol Prog Ser 168:285\u2013296","journal-title":"Mar Ecol Prog Ser"},{"key":"10745_CR296","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"issue":"2","key":"10745_CR297","first-page":"35","volume":"19","author":"Y Soh","year":"2018","unstructured":"Soh Y, Song J, Hae Y (2018) Multiple plankton detection and recognition in microscopic images with homogeneous clumping and heterogeneous interspersion. J Instit Converg Signal Process 19(2):35\u201341","journal-title":"J Instit Converg Signal Process"},{"key":"10745_CR298","doi-asserted-by":"crossref","unstructured":"Solano GA, Gasmen P, Marquez EJ (2018) Radiolarian classification decision support using supervised and unsupervised learning approaches. International conference on information. Intelligence, systems and applications (IISA), pp 1\u20136","DOI":"10.1109\/IISA.2018.8633617"},{"key":"10745_CR299","doi-asserted-by":"publisher","first-page":"309","DOI":"10.3354\/meps216309","volume":"216","author":"A Solow","year":"2001","unstructured":"Solow A, Davis C, Hu Q (2001) Estimating the taxonomic composition of a sample when individuals are classified with error. Mar Ecol Prog Ser 216:309\u2013311","journal-title":"Mar Ecol Prog Ser"},{"issue":"7","key":"10745_CR300","first-page":"1","volume":"11","author":"H Song","year":"2020","unstructured":"Song H, Mehdi SR, Huang H et al (2020) Classification of freshwater zooplankton by pre-trained convolutional neural network in underwater microscopy. Int J Adv Comput Sci Appl 11(7):1\u20137","journal-title":"Int J Adv Comput Sci Appl"},{"key":"10745_CR301","doi-asserted-by":"publisher","first-page":"204","DOI":"10.4319\/lom.2007.5.204","volume":"5","author":"HM Sosik","year":"2007","unstructured":"Sosik HM, Olson RJ (2007) Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry. Limnol Oceanogr Methods 5:204\u2013216","journal-title":"Limnol Oceanogr Methods"},{"key":"10745_CR302","doi-asserted-by":"publisher","unstructured":"Sosik HM, Peacock EE, Brownlee EF (2021) WHOI-plankton: annotated plankton images - dataset for developing and evaluating classification methods. https:\/\/doi.org\/10.1575\/1912\/7341","DOI":"10.1575\/1912\/7341"},{"issue":"4","key":"10745_CR303","doi-asserted-by":"publisher","first-page":"3588","DOI":"10.1109\/TIE.2020.2977553","volume":"68","author":"X Sun","year":"2020","unstructured":"Sun X, Xv H, Dong J et al (2020) Few-shot learning for domain-specific fine-grained image classification. IEEE Trans Ind Electr 68(4):3588\u20133598","journal-title":"IEEE Trans Ind Electr"},{"key":"10745_CR304","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, et\u00a0al (2015) Going deeper with convolutions. In: Conference on computer vision and pattern recognition (CVPR), pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"10745_CR305","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, et\u00a0al (2016) Rethinking the inception architecture for computer vision. In: Conference on computer vision and pattern recognition (CVPR), pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"10745_CR306","doi-asserted-by":"crossref","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, et\u00a0al (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: AAAI Conference on Artificial Intelligence","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"10745_CR307","doi-asserted-by":"crossref","unstructured":"S\u00f6mek B, Yuksel SE (2023) Plankton classification with deep learning. In: 2023 Signal processing: algorithms, architectures, arrangements, and applications (SPA), pp 118\u2013123","DOI":"10.23919\/SPA59660.2023.10274456"},{"key":"10745_CR308","unstructured":"Tan M, Le Q (2019) Efficientnet: rethinking model scaling for convolutional neural networks. In: International conference on machine learning (ICML), pp 6105\u20136114"},{"key":"10745_CR309","doi-asserted-by":"publisher","first-page":"5119","DOI":"10.1016\/j.ijleo.2014.04.079","volume":"125","author":"S Tan","year":"2014","unstructured":"Tan S, Zhang F, Huang Q et al (2014) Measuring and calculating geometrical parameters of marine plankton using digital laser holographic imaging. Optik 125:5119\u20135123","journal-title":"Optik"},{"key":"10745_CR310","unstructured":"Tanaka FHKdS, Aranha C (2019) Data augmentation using gans. arXiv preprint arXiv:1904.09135"},{"issue":"1\u20133","key":"10745_CR311","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1023\/A:1006517211724","volume":"12","author":"X Tang","year":"1998","unstructured":"Tang X, Stewart WK, Huang H et al (1998) Automatic plankton image recognition. Artif Intell Rev 12(1\u20133):177\u2013199","journal-title":"Artif Intell Rev"},{"issue":"3","key":"10745_CR312","doi-asserted-by":"publisher","first-page":"728","DOI":"10.1109\/JOE.2004.836995","volume":"31","author":"X Tang","year":"2006","unstructured":"Tang X, Lin F, Samson S et al (2006) Binary plankton image classification. IEEE J Oceanic Eng 31(3):728\u2013735","journal-title":"IEEE J Oceanic Eng"},{"key":"10745_CR313","unstructured":"Teigen AL, Saad A, Stahl A (2020) Leveraging similarity metrics to in-situ discover planktonic interspecies variations or mutations. In: Global Oceans 2020: Singapore\u2013US Gulf Coast, IEEE, pp 1\u20138"},{"key":"10745_CR314","doi-asserted-by":"crossref","unstructured":"Teuwen J, Moriakov N (2020) Convolutional neural networks. In: Handbook of medical image computing and computer assisted intervention. Elsevier, pp 481\u2013501","DOI":"10.1016\/B978-0-12-816176-0.00025-9"},{"issue":"10","key":"10745_CR315","doi-asserted-by":"publisher","first-page":"2398","DOI":"10.1016\/0043-1354(95)00053-N","volume":"29","author":"SU Thiel","year":"1995","unstructured":"Thiel SU, Wiltshire RJ, Davies LJ (1995) Automated object recognition of blue-green algae for measuring water quality-a preliminary study. Water Res 29(10):2398\u20132404","journal-title":"Water Res"},{"key":"10745_CR316","doi-asserted-by":"crossref","unstructured":"Tountas K, Pados DA, Medley MJ (2019) Conformity evaluation and l1-norm principal-component analysis of tensor data. In: Big data: learning, analytics, and applications, pp 190\u2013200","DOI":"10.1117\/12.2520538"},{"key":"10745_CR317","doi-asserted-by":"crossref","unstructured":"Tsechpenakis G, Guigand CM, Cowen RK (2007) Image analysis techniques to accompany a new in situ ichthyoplankton imaging system. In: OCEANS Conference, IEEE, pp 1\u20136","DOI":"10.1109\/OCEANSE.2007.4302271"},{"key":"10745_CR318","doi-asserted-by":"publisher","first-page":"106775","DOI":"10.1016\/j.cmpb.2022.106775","volume":"219","author":"N Vallez","year":"2022","unstructured":"Vallez N, Bueno G, Deniz O et al (2022) Diffeomorphic transforms for data augmentation of highly variable shape and texture objects. Comput Methods Programs Biomed 219:106775","journal-title":"Comput Methods Programs Biomed"},{"key":"10745_CR319","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1016\/j.patcog.2016.06.005","volume":"61","author":"N Van Noord","year":"2017","unstructured":"Van Noord N, Postma E (2017) Learning scale-variant and scale-invariant features for deep image classification. Patt Recogn 61:583\u2013592","journal-title":"Patt Recogn"},{"key":"10745_CR320","doi-asserted-by":"crossref","unstructured":"Varma K, Nyman L, Tountas K, et\u00a0al (2020) Autonomous plankton classification from reconstructed holographic imagery by l1-pca-assisted convolutional neural networks. In: Global Oceans 2020: Singapore\u2013US Gulf Coast, IEEE, pp 1\u20136","DOI":"10.1109\/IEEECONF38699.2020.9389240"},{"key":"10745_CR321","doi-asserted-by":"crossref","unstructured":"Venkataramanan A, Laviale M, Figus C, et\u00a0al (2021) Tackling inter-class similarity and intra-class variance for microscopic image-based classification. In: International conference on computer vision systems (ICVS), Springer, pp 93\u2013103","DOI":"10.1007\/978-3-030-87156-7_8"},{"key":"10745_CR322","doi-asserted-by":"publisher","first-page":"1659","DOI":"10.1016\/j.patcog.2011.10.019","volume":"45","author":"A Verikas","year":"2012","unstructured":"Verikas A, Gelzinis A, Bacauskiene M et al (2012) Phase congruency-based detection of circular objects applied to analysis of phytoplankton images. Patt Recogn 45:1659\u20131670","journal-title":"Patt Recogn"},{"issue":"2","key":"10745_CR323","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1109\/JOE.2014.2317955","volume":"40","author":"A Verikas","year":"2015","unstructured":"Verikas A, Gelzinis A, Bacauskiene M et al (2015) An integrated approach to analysis of phytoplankton images. IEEE J Oceanic Eng 40(2):315\u2013326","journal-title":"IEEE J Oceanic Eng"},{"key":"10745_CR324","unstructured":"Wacquet G, Lefebvre A, Blondel C, et\u00a0al (2018) Combination of machine learning methodologies and imaging-in-flow systems for the automated detection of harmful algae. In: Harmful Algae 2018 - From Ecosystems to Socioecosystems: International Conference on Harmful Algae"},{"issue":"9","key":"10745_CR325","doi-asserted-by":"publisher","first-page":"516","DOI":"10.1002\/lom3.10379","volume":"18","author":"NL Walcutt","year":"2020","unstructured":"Walcutt NL, Kn\u00f6rlein B, Cetini\u0107 I et al (2020) Assessment of holographic microscopy for quantifying marine particle size and concentration. Limnol Oceanogr Methods 18(9):516\u2013530","journal-title":"Limnol Oceanogr Methods"},{"key":"10745_CR326","doi-asserted-by":"crossref","unstructured":"Walker JL, Orenstein EC (2021) Improving rare-class recognition of marine plankton with hard negative mining. In: International conference on computer vision (ICCV), pp 3672\u20133682","DOI":"10.1109\/ICCVW54120.2021.00410"},{"issue":"2","key":"10745_CR327","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/S0167-7012(02)00057-X","volume":"51","author":"RF Walker","year":"2002","unstructured":"Walker RF, Ishikawa K, Kumagai M (2002) Fluorescence-assisted image analysis of freshwater microalgae. J Microbiol Methods 51(2):149\u2013162","journal-title":"J Microbiol Methods"},{"key":"10745_CR328","doi-asserted-by":"crossref","unstructured":"Wang C, Yu Z, Zheng H, et\u00a0al (2017) Cgan-plankton: towards large-scale imbalanced class generation and fine-grained classification. In: International conference on image processing (ICIP), IEEE, pp 855\u2013859","DOI":"10.1109\/ICIP.2017.8296402"},{"key":"10745_CR329","doi-asserted-by":"crossref","unstructured":"Wang C, Zheng X, Guo C, et\u00a0al (2018) Transferred parallel convolutional neural network for large imbalanced plankton database classification. In: OCEANS Techno-Oceans (OTO), IEEE, pp 1\u20135","DOI":"10.1109\/OCEANSKOBE.2018.8558836"},{"key":"10745_CR330","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3178128","author":"J Wang","year":"2022","unstructured":"Wang J, Lan C, Liu C et al (2022) Generalizing to unseen domains: a survey on domain generalization. IEEE Trans Knowl Data Eng. https:\/\/doi.org\/10.1109\/TKDE.2022.3178128","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"10745_CR331","doi-asserted-by":"crossref","unstructured":"Wang J, Tang C, Li J (2022b) Towards real-time analysis of marine phytoplankton images sampled at high frame rate by a yolox-based object detection algorithm. In: OCEANS 2022-Chennai, IEEE, pp 1\u20139","DOI":"10.1109\/OCEANSChennai45887.2022.9775330"},{"issue":"12","key":"10745_CR332","doi-asserted-by":"publisher","first-page":"2591","DOI":"10.1109\/TCSVT.2016.2589879","volume":"27","author":"K Wang","year":"2016","unstructured":"Wang K, Zhang D, Li Y et al (2016) Cost-effective active learning for deep image classification. IEEE Trans Circuits Syst Video Technol 27(12):2591\u20132600","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"10745_CR333","doi-asserted-by":"crossref","unstructured":"Watson J (2018) High-resolution underwater holographic imaging. In: Encyclopedia of modern optics. pp 106\u2013112","DOI":"10.1016\/B978-0-12-803581-8.09612-0"},{"key":"10745_CR334","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/3783977","author":"L Wei","year":"2022","unstructured":"Wei L, XiaoPan S, Heydari F (2022) Microalgae classification using improved metaheuristic algorithm. Math Probl Eng. https:\/\/doi.org\/10.1155\/2022\/3783977","journal-title":"Math Probl Eng"},{"issue":"1","key":"10745_CR335","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1186\/s40537-016-0043-6","volume":"3","author":"K Weiss","year":"2016","unstructured":"Weiss K, Khoshgoftaar TM, Wang D (2016) A survey of transfer learning. J Big Data 3(1):9","journal-title":"J Big Data"},{"key":"10745_CR336","doi-asserted-by":"crossref","unstructured":"Wen Y, Zhang K, Li Z, et\u00a0al (2016) A discriminative feature learning approach for deep face recognition. In: European conference on computer vision (ECCV), pp 499\u2013515","DOI":"10.1007\/978-3-319-46478-7_31"},{"issue":"5","key":"10745_CR337","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3400066","volume":"11","author":"G Wilson","year":"2020","unstructured":"Wilson G, Cook DJ (2020) A survey of unsupervised deep domain adaptation. ACM Trans Intell Syst Technol 11(5):1\u201346","journal-title":"ACM Trans Intell Syst Technol"},{"issue":"5800","key":"10745_CR338","doi-asserted-by":"publisher","first-page":"787","DOI":"10.1126\/science.1132294","volume":"314","author":"B Worm","year":"2006","unstructured":"Worm B, Barbier EB, Beaumont N et al (2006) Impacts of biodiversity loss on ocean ecosystem services. Science 314(5800):787\u2013790","journal-title":"Science"},{"issue":"8","key":"10745_CR339","doi-asserted-by":"publisher","first-page":"858","DOI":"10.1109\/34.709610","volume":"20","author":"MF Wu","year":"1998","unstructured":"Wu MF, Sheu HT (1998) Representation of 3d surfaces by two-variable Fourier descriptors. IEEE Trans Patt Anal Mach Intell (PAMI) 20(8):858\u2013863","journal-title":"IEEE Trans Patt Anal Mach Intell (PAMI)"},{"key":"10745_CR340","doi-asserted-by":"publisher","first-page":"125438","DOI":"10.1109\/ACCESS.2020.3007646","volume":"8","author":"Q Xiaoyan","year":"2020","unstructured":"Xiaoyan Q (2020) Research on imbalanced microscopic image classification of harmful algae. IEEE Access 8:125438\u2013125446","journal-title":"IEEE Access"},{"key":"10745_CR341","doi-asserted-by":"crossref","unstructured":"Xu L, Xu L, Chen Y, et\u00a0al (2022) Accurate classification of algae using deep convolutional neural network with a small database. ACS ES &T Water","DOI":"10.1021\/acsestwater.1c00466"},{"key":"10745_CR342","doi-asserted-by":"crossref","unstructured":"Yan J, Li X, Cui Z (2017) A more efficient cnn architecture for plankton classification. In: Chinese conference on computer vision (CCCV), Springer, pp 198\u2013208","DOI":"10.1007\/978-981-10-7305-2_18"},{"issue":"6","key":"10745_CR343","doi-asserted-by":"publisher","first-page":"15311","DOI":"10.1007\/s11356-022-23280-6","volume":"30","author":"M Yang","year":"2023","unstructured":"Yang M, Wang W, Gao Q et al (2023) Automatic identification of harmful algae based on multiple convolutional neural networks and transfer learning. Environ Sci Pollut Res 30(6):15311\u201315324","journal-title":"Environ Sci Pollut Res"},{"issue":"10","key":"10745_CR344","doi-asserted-by":"publisher","first-page":"2643","DOI":"10.1093\/icesjms\/fsac198","volume":"79","author":"Z Yang","year":"2022","unstructured":"Yang Z, Li J, Chen T et al (2022) Contrastive learning-based image retrieval for automatic recognition of in situ marine plankton images. ICES J Mar Sci 79(10):2643\u20132655","journal-title":"ICES J Mar Sci"},{"key":"10745_CR345","doi-asserted-by":"publisher","first-page":"185","DOI":"10.3354\/meps09387","volume":"441","author":"L Ye","year":"2011","unstructured":"Ye L, Chang CY, Hsieh Ch (2011) Bayesian model for semi-automated zooplankton classification with predictive confidence and rapid category aggregation. Mar Ecol Prog Ser 441:185\u2013196","journal-title":"Mar Ecol Prog Ser"},{"issue":"6","key":"10745_CR346","doi-asserted-by":"publisher","first-page":"2872","DOI":"10.1109\/TPAMI.2021.3054775","volume":"44","author":"M Ye","year":"2021","unstructured":"Ye M, Shen J, Lin G et al (2021) Deep learning for person re-identification: a survey and outlook. IEEE Trans Patt Anal Mach Intell (PAMI) 44(6):2872\u20132893","journal-title":"IEEE Trans Patt Anal Mach Intell (PAMI)"},{"key":"10745_CR347","first-page":"284","volume":"15","author":"K Yu","year":"2023","unstructured":"Yu K, Sun W (2023) Annular characteristic spectrum extraction for species identification of marine coscinodiscus from micrographs. J Biotech Res 15:284\u2013294","journal-title":"J Biotech Res"},{"key":"10745_CR348","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/3783977","author":"A Yuan","year":"2023","unstructured":"Yuan A, Wang B, Li J et al (2023) A low-cost edge AI-chip-based system for real-time algae species classification and hab prediction. Water Res. https:\/\/doi.org\/10.1155\/2022\/3783977","journal-title":"Water Res"},{"issue":"11","key":"10745_CR349","doi-asserted-by":"publisher","first-page":"757","DOI":"10.4319\/lom.2014.12.757","volume":"12","author":"EM Zetsche","year":"2014","unstructured":"Zetsche EM, El Mallahi A, Dubois F et al (2014) Imaging-in-flow: Digital holographic microscopy as a novel tool to detect and classify nanoplanktonic organisms. Limnol Oceanogr Methods 12(11):757\u2013775","journal-title":"Limnol Oceanogr Methods"},{"key":"10745_CR350","doi-asserted-by":"crossref","unstructured":"Zhang J, Li C, Yin Y, et\u00a0al (2022) Applications of artificial neural networks in microorganism image analysis: a comprehensive review from conventional multilayer perceptron to popular convolutional neural network and potential visual transformer. Artificial Intelligence Review, pp 1\u201358","DOI":"10.1007\/s10462-022-10192-7"},{"key":"10745_CR351","doi-asserted-by":"publisher","first-page":"106979","DOI":"10.1016\/j.optlastec.2021.106979","volume":"139","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Lu Y, Wang H et al (2021) Automatic classification of marine plankton with digital holography using convolutional neural network. Optics Laser Technol 139:106979","journal-title":"Optics Laser Technol"},{"key":"10745_CR352","unstructured":"Zhao F, Tang X, Lin F, et\u00a0al (2005) Binary plankton image classification using random subspace. In: International conference on image processing (ICIP), IEEE, pp 1\u2013357"},{"key":"10745_CR353","doi-asserted-by":"crossref","unstructured":"Zhao F, Lin F, Seah HS (2009) Bagging based plankton image classification. In: IEEE International conference on image processing (ICIP), IEEE, pp 2081\u20132084","DOI":"10.1109\/ICIP.2009.5414357"},{"key":"10745_CR354","doi-asserted-by":"publisher","first-page":"1853","DOI":"10.1016\/j.neucom.2009.12.033","volume":"73","author":"F Zhao","year":"2010","unstructured":"Zhao F, Lin F, Seah HS (2010) Binary sipper plankton image classification using random subspace. Neurocomputing 73:1853\u20131860","journal-title":"Neurocomputing"},{"key":"10745_CR355","unstructured":"Zheng A, Wang M (2015) Convolutional neural networksbased plankton image classification system. Project Report, Course CSE258 Web Mining and Recommender Systems, University of California San Diego, USA, http:\/\/jmcauley.ucsd.edu\/cse258\/projects\/fa15\/005.pdf"},{"issue":"16","key":"10745_CR356","doi-asserted-by":"publisher","first-page":"570","DOI":"10.1186\/s12859-017-1954-8","volume":"18","author":"H Zheng","year":"2017","unstructured":"Zheng H, Wang R, Yu Z et al (2017) Automatic plankton image classification combining multiple view features via multiple kernel learning. BMC Bioinform 18(16):570","journal-title":"BMC Bioinform"},{"issue":"4","key":"10745_CR357","first-page":"4396","volume":"45","author":"K Zhou","year":"2022","unstructured":"Zhou K, Liu Z, Qiao Y et al (2022) Domain generalization: a survey. IEEE Trans Patt Anal Mach Intell 45(4):4396\u20134415","journal-title":"IEEE Trans Patt Anal Mach Intell"},{"key":"10745_CR358","doi-asserted-by":"publisher","first-page":"105641","DOI":"10.1016\/j.envsoft.2023.105641","volume":"162","author":"X Zhou","year":"2023","unstructured":"Zhou X, Rowe M, Liu Q et al (2023) Comparison of Eulerian and Lagrangian transport models for harmful algal bloom forecasts in lake erie. Environ Modell Softw 162:105641","journal-title":"Environ Modell Softw"},{"key":"10745_CR359","doi-asserted-by":"crossref","unstructured":"Zhu JY, Park T, Isola P, et\u00a0al (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: International conference on computer vision (ICCV), pp 2223\u20132232","DOI":"10.1109\/ICCV.2017.244"},{"key":"10745_CR360","unstructured":"Zimmerman TG, Pastore VP, Biswas SK, et\u00a0al (2020) Embedded system to detect, track and classify plankton using a lensless video microscope. arXiv preprint arXiv:2005.13064"},{"key":"10745_CR361","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.ecss.2015.05.024","volume":"162","author":"A Zingone","year":"2015","unstructured":"Zingone A, Harrison PJ, Kraberg A et al (2015) Increasing the quality, comparability and accessibility of phytoplankton species composition time-series data. Estuar Coast Shelf Sci 162:151\u2013160","journal-title":"Estuar Coast Shelf Sci"},{"key":"10745_CR362","doi-asserted-by":"publisher","first-page":"1789","DOI":"10.1007\/s13762-018-2108-x","volume":"16","author":"E Zohdi","year":"2019","unstructured":"Zohdi E, Abbaspour M (2019) Harmful algal blooms (red tide): a review of causes, impacts and approaches to monitoring and prediction. Int J Environ Sci Technol 16:1789\u20131806","journal-title":"Int J Environ Sci Technol"},{"key":"10745_CR363","doi-asserted-by":"crossref","unstructured":"Zoph B, Vasudevan V, Shlens J, et\u00a0al (2018) Learning transferable architectures for scalable image recognition. In: Conference on computer vision and pattern recognition (CVPR), pp 8697\u20138710","DOI":"10.1109\/CVPR.2018.00907"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-024-10745-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-024-10745-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-024-10745-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,17]],"date-time":"2024-05-17T06:10:06Z","timestamp":1715926206000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-024-10745-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,12]]},"references-count":364,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["10745"],"URL":"https:\/\/doi.org\/10.1007\/s10462-024-10745-y","relation":{},"ISSN":["1573-7462"],"issn-type":[{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,12]]},"assertion":[{"value":"6 March 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 April 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"All authors consent that the publisher has the author\u2019s permission to publish research findings. All authors guarantee that the research findings have not been previously published.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"114"}}