{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:46:51Z","timestamp":1760402811622,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,5,2]],"date-time":"2020-05-02T00:00:00Z","timestamp":1588377600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100017610","name":"Shenzhen Science and Technology Innovation Program","doi-asserted-by":"publisher","award":["JCYJ20170412171011187","JCYJ20160428182026575"],"award-info":[{"award-number":["JCYJ20170412171011187","JCYJ20160428182026575"]}],"id":[{"id":"10.13039\/501100017610","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61527826","51735002"],"award-info":[{"award-number":["61527826","51735002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFC1403602"],"award-info":[{"award-number":["2017YFC1403602"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this study, we propose a method for training convolutional neural networks to make them identify and classify images with higher classification accuracy. By combining the Cartesian and polar coordinate systems when describing the images, the method of recognition and classification for plankton images is discussed. The optimized classification and recognition networks are constructed. They are available for in situ plankton images, exploiting the advantages of both coordinate systems in the network training process. Fusing the two types of vectors and using them as the input for conventional machine learning models for classification, support vector machines (SVMs) are selected as the classifiers to combine these two features of vectors, coming from different image coordinate descriptions. The accuracy of the proposed model was markedly higher than those of the initial classical convolutional neural networks when using the in situ plankton image data, with the increases in classification accuracy and recall rate being 5.3% and 5.1% respectively. In addition, the proposed training method can improve the classification performance considerably when used on the public CIFAR-10 dataset.<\/jats:p>","DOI":"10.3390\/s20092592","type":"journal-article","created":{"date-parts":[[2020,5,4]],"date-time":"2020-05-04T14:00:43Z","timestamp":1588600843000},"page":"2592","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Method for Training Convolutional Neural Networks for In Situ Plankton Image Recognition and Classification Based on the Mechanisms of the Human Eye"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2150-776X","authenticated-orcid":false,"given":"Xuemin","family":"Cheng","sequence":"first","affiliation":[{"name":"Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Ren","sequence":"additional","affiliation":[{"name":"Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaichang","family":"Cheng","sequence":"additional","affiliation":[{"name":"Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qun","family":"Hao","sequence":"additional","affiliation":[{"name":"School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.ecss.2016.10.045","article-title":"Plankton bioindicators of environmental conditions in coastal lagoons","volume":"184","author":"Hemraj","year":"2017","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1093\/plankt\/fbz023","article-title":"Automatic plankton quantification using deep features","volume":"41","author":"Peacock","year":"2019","journal-title":"J. Plankton Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/S0079-6611(02)00140-4","article-title":"From the Hensen net toward four-dimensional biological oceanography","volume":"56","author":"Wiebe","year":"2003","journal-title":"Prog. Oceanogr."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.marpol.2017.05.022","article-title":"From microscope to management: The critical value of plankton taxonomy to marine policy and biodiversity conservation","volume":"83","author":"Johns","year":"2017","journal-title":"Mar. Policy"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.pocean.2004.07.001","article-title":"The role of seawater constituents in light backscattering in the ocean","volume":"61","author":"Stramski","year":"2004","journal-title":"Prog. Oceanogr."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1109\/LSP.2018.2792050","article-title":"Emerging from water: Underwater image color correction based on weakly supervised color transfer","volume":"25","author":"Li","year":"2018","journal-title":"IEEE Signal Process Lett."},{"key":"ref_7","first-page":"1894","article-title":"Automatic recognition method of zooplankton image in dark field","volume":"29","author":"Tian","year":"2019","journal-title":"Rev. Cient. Fac. Cienc. Vet."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4903","DOI":"10.1364\/OL.43.004903","article-title":"Deeply seeing through highly turbid water by active polarization imaging","volume":"43","author":"Liu","year":"2018","journal-title":"Opt. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1023\/A:1006517211724","article-title":"Automatic plankton image recognition","volume":"12","author":"Tang","year":"1998","journal-title":"Artif. Intell. Rev."},{"key":"ref_10","first-page":"589","article-title":"Active learning to recognize multiple types of plankton","volume":"6","author":"Luo","year":"2005","journal-title":"J Mach. Learn. Res."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ellen, J., Li, H., and Ohman, M.D. (2015, January 19\u201322). Quantifying California current plankton samples with efficient machine learning techniques. Proceedings of the OCEANS 2015-MTS\/IEEE Washington, Washington, DC, USA.","DOI":"10.23919\/OCEANS.2015.7404607"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Voulodimos, A., Doulamis, N., Doulamis, A., and Protopapadakis, E. (2018). Deep learning for computer vision: A brief review. Comput. Intell. Neurosci., 2018.","DOI":"10.1155\/2018\/7068349"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Moniruzzaman, M., Islam SM, S., Bennamoun, M., and Lavery, P. (2017). Deep learning on underwater marine object detection: A survey. International Conference on Advanced Concepts for Intelligent Vision Systems, Springer.","DOI":"10.1007\/978-3-319-70353-4_13"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2352","DOI":"10.1162\/neco_a_00990","article-title":"Deep convolutional neural networks for image classification: A comprehensive review","volume":"29","author":"Rawat","year":"2017","journal-title":"Neural Comput."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_16","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.neucom.2015.09.116","article-title":"Deep learning for visual understanding: A review","volume":"187","author":"Guo","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_18","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_20","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_21","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (July, January 22). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA."},{"key":"ref_22","unstructured":"Kauderer-Abrams, E. (2017). Quantifying translation-invariance in convolutional neural networks. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bi, H., Guo, Z., Benfield, M.C., Fan, C., Ford, M., Shahrestani, S., and Sieracki, J.M. (2015). A semi-automated image analysis procedure for in situ plankton imaging systems. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0127121"},{"key":"ref_24","unstructured":"Krizhevsky, A., Nair, V., and Hinton, G. (2019, June 01). The CIFAR-10 Dataset. Available online: https:\/\/www.cs.toronto.edu\/~kriz\/cifar.html."},{"key":"ref_25","unstructured":"Perez, L., and Wang, J. (2017). The effectiveness of data augmentation in image classification using deep learning. arXiv."},{"key":"ref_26","unstructured":"(2020, January 23). Human Eye: Additional Images. Available online: https:\/\/en.wikipedia.org\/wiki\/Eye."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.pacs.2016.05.001","article-title":"Photoacoustic imaging of the eye: A mini review","volume":"4","author":"Liu","year":"2016","journal-title":"Photoacoustics"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1126\/science.aal5060","article-title":"Regenerating optic pathways from the eye to the brain","volume":"356","author":"Laha","year":"2017","journal-title":"Science"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ramesh, B., Yang, H., Orchard, G.M., Le Thi, N.A., Zhang, S., and Xiang, C. (2019). DART: Distribution aware retinal transform for event-based cameras. IEEE Trans. Pattern Anal. Mach. Intell.","DOI":"10.1109\/TPAMI.2019.2919301"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"90","DOI":"10.13176\/11.355","article-title":"Image registration using log polar transform and phase correlation to recover higher scale","volume":"7","author":"Sarvaiya","year":"2012","journal-title":"J. Pattern Recognit. Res."},{"key":"ref_31","unstructured":"Wolberg, G., and Zokai, S. (2000, January 10\u201313). Robust image registration using log-polar transform. Proceedings of the 2000 International Conference on Image Processing (Cat. No. 00CH37101), Vancouver, BC, Canada."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"21","DOI":"10.3354\/meps295021","article-title":"Automatic plankton image recognition with co-occurrence matrices and Support Vector Machine","volume":"295","author":"Hu","year":"2005","journal-title":"Mar. Ecol. Prog. Ser."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"51","DOI":"10.3354\/meps306051","article-title":"Accurate automatic quantification of taxa-specific plankton abundance using dual classification with correction","volume":"306","author":"Hu","year":"2006","journal-title":"Mar. Ecol. Prog. Ser."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1023\/A:1018628609742","article-title":"Least squares support vector machine classifiers","volume":"9","author":"Suykens","year":"1999","journal-title":"Neural Process. Lett."},{"key":"ref_35","first-page":"1","article-title":"A unified view on multi-class support vector classification","volume":"17","author":"Dogan","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Fefilatyev, S., Kramer, K., Hall, L., Goldgof, D., Kasturi, R., Remsen, A., and Daly, K. (2011, January 11). Detection of anomalous particles from the deepwater horizon oil spill using the SIPPER3 underwater imaging platform. Proceedings of the IEEE International Conference on Data Mining Workshops, Vancouver, BC, Canada.","DOI":"10.1109\/ICDMW.2011.65"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Py, O., Hong, H., and Zhongzhi, S. (2016, January 20\u201322). Plankton classification with deep convolutional neural networks. Proceedings of the Information Technology, Networking, Electronic & Automation Control Conference, Chongqing, China.","DOI":"10.1109\/ITNEC.2016.7560334"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Cheng, K., Cheng, X., Wang, Y., Bi, H., and Benfield, M.C. (2019). Enhanced convolutional neural network for plankton identification and enumeration. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0219570"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/9\/2592\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:09:50Z","timestamp":1760364590000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/9\/2592"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,2]]},"references-count":38,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2020,5]]}},"alternative-id":["s20092592"],"URL":"https:\/\/doi.org\/10.3390\/s20092592","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2020,5,2]]}}}