{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:39:41Z","timestamp":1753886381779,"version":"3.37.3"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2022,6,29]],"date-time":"2022-06-29T00:00:00Z","timestamp":1656460800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,6,29]],"date-time":"2022-06-29T00:00:00Z","timestamp":1656460800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 11871104"],"award-info":[{"award-number":["No. 11871104"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 12131006"],"award-info":[{"award-number":["No. 12131006"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"DOI":"10.1007\/s10489-022-03849-x","type":"journal-article","created":{"date-parts":[[2022,6,29]],"date-time":"2022-06-29T22:02:42Z","timestamp":1656540162000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Image classification based on quaternion-valued capsule network"],"prefix":"10.1007","author":[{"given":"Heng","family":"Zhou","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6253-2446","authenticated-orcid":false,"given":"Chunlei","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Qiaoyu","family":"Ma","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,29]]},"reference":[{"key":"3849_CR1","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1016\/j.neucom.2020.07.053","volume":"417","author":"M Alam","year":"2020","unstructured":"Alam M, Samad MD, Vidyaratne L, Glandon A, Iftekharuddin KM (2020) Survey on deep neural networks in speech and vision systems. Neurocomputing 417:302\u2013321. https:\/\/doi.org\/10.1016\/j.neucom.2020.07.053","journal-title":"Neurocomputing"},{"key":"3849_CR2","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https:\/\/doi.org\/10.1109\/cvpr.2016.90, pp 770\u2013778","DOI":"10.1109\/cvpr.2016.90"},{"key":"3849_CR3","doi-asserted-by":"publisher","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https:\/\/doi.org\/10.1109\/cvpr.2015.7298594, pp 1\u20139","DOI":"10.1109\/cvpr.2015.7298594"},{"key":"3849_CR4","doi-asserted-by":"publisher","unstructured":"Hinton GE, Krizhevsky A, Wang SD (2011) Transforming auto-encoders. In: Artificial Neural Networks and Machine Learning \u2013 ICANN 2011. https:\/\/doi.org\/10.1007\/978-3-642-21735-7_6. Springer, pp 44\u201351","DOI":"10.1007\/978-3-642-21735-7_6"},{"key":"3849_CR5","unstructured":"Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. In: Proceedings of the 31st international conference on neural information processing systems, pp 3859\u20133869"},{"key":"3849_CR6","unstructured":"Hinton GE, Sabour S, Frosst N (2018) Matrix capsules with EM routing. In: 6th International conference on learning representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018. Conference Track Proceedings. https:\/\/openreview.net\/forum?id=HJWLfGWRb, pp 1\u201315"},{"key":"3849_CR7","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.media.2020.101889","volume":"68","author":"R LaLonde","year":"2021","unstructured":"LaLonde R, Xu Z, Irmakci I, Jain S, Bagci U (2021) Capsules for biomedical image segmentation. Med Image Anal 68:89\u2013101908. https:\/\/doi.org\/10.1016\/j.media.2020.101889","journal-title":"Med Image Anal"},{"issue":"19","key":"3849_CR8","doi-asserted-by":"publisher","first-page":"4974","DOI":"10.3390\/cancers13194974","volume":"13","author":"E P\u00e9rez","year":"2021","unstructured":"P\u00e9rez E, Ventura S (2021) Melanoma recognition by fusing convolutional blocks and dynamic routing between capsules. Cancers 13(19):4974\u20134993. https:\/\/doi.org\/10.3390\/cancers13194974","journal-title":"Cancers"},{"key":"3849_CR9","doi-asserted-by":"publisher","unstructured":"Parcollet T, Morchid M, Linar\u00e8s G (2019) Quaternion convolutional neural networks for heterogeneous image processing. In: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https:\/\/doi.org\/10.1109\/ICASSP.2019.8682495, pp 8514\u20138518","DOI":"10.1109\/ICASSP.2019.8682495"},{"key":"3849_CR10","doi-asserted-by":"crossref","unstructured":"Jing B, Prabhu V, Gu A, Whaley J (2021) Rotation-invariant gait identification with quaternion convolutional neural networks (student abstract). In: Proceedings of the AAAI conference on artificial intelligence, vol 35. pp 15805\u201315806. https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/17899","DOI":"10.1609\/aaai.v35i18.17899"},{"key":"3849_CR11","doi-asserted-by":"publisher","unstructured":"Grassucci E, Comminiello D, Uncini A (2021) A quaternion-valued variational autoencoder. In: ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https:\/\/doi.org\/10.1109\/ICASSP39728.2021.9413859, pp 3310\u20133314","DOI":"10.1109\/ICASSP39728.2021.9413859"},{"issue":"1","key":"3849_CR12","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1109\/TNNLS.2018.2829526","volume":"30","author":"M Xiang","year":"2018","unstructured":"Xiang M, Dees BS, Mandic DP (2018) Multiple-model adaptive estimation for 3-d and 4-d signals: A widely linear quaternion approach. IEEE Trans Neural Netw Learn Syst 30(1):72\u201384. https:\/\/doi.org\/10.1109\/TNNLS.2018.2829526","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"3849_CR13","doi-asserted-by":"publisher","unstructured":"Gu J, Tresp V, Hu H (2021) Capsule network is not more robust than convolutional network. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https:\/\/doi.org\/10.1109\/CVPR46437.2021.01408, pp 14304\u201314312","DOI":"10.1109\/CVPR46437.2021.01408"},{"key":"3849_CR14","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1016\/j.neucom.2021.08.064","volume":"463","author":"A Byerly","year":"2021","unstructured":"Byerly A, Kalganova T, Dear I (2021) No routing needed between capsules. Neurocomputing 463:545\u2013553. https:\/\/doi.org\/10.1016\/j.neucom.2021.08.064","journal-title":"Neurocomputing"},{"key":"3849_CR15","doi-asserted-by":"publisher","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https:\/\/doi.org\/10.1109\/CVPR.2017.243, pp 2261\u20132269","DOI":"10.1109\/CVPR.2017.243"},{"key":"3849_CR16","doi-asserted-by":"publisher","unstructured":"Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https:\/\/doi.org\/10.1109\/CVPR.2017.195, pp 1800\u20131807","DOI":"10.1109\/CVPR.2017.195"},{"key":"3849_CR17","doi-asserted-by":"publisher","unstructured":"Zhang T, Qi G, Xiao B, Wang J (2017) Interleaved group convolutions. In: IEEE International conference on computer vision, ICCV 2017, Venice, Italy, October 22-29, 2017. https:\/\/doi.org\/10.1109\/ICCV.2017.469, pp 4383\u20134392","DOI":"10.1109\/ICCV.2017.469"},{"key":"3849_CR18","doi-asserted-by":"publisher","unstructured":"Zhang X, Zhou X, Lin M, Sun J (2018) Shufflenet: An extremely efficient convolutional neural network for mobile devices. In: 2018 IEEE\/CVF conference on computer vision and pattern recognition. https:\/\/doi.org\/10.1109\/CVPR.2018.00716, pp 6848\u20136856","DOI":"10.1109\/CVPR.2018.00716"},{"key":"3849_CR19","doi-asserted-by":"crossref","unstructured":"Ma N, Zhang X, Zheng H-T , Sun J (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":"3849_CR20","doi-asserted-by":"publisher","unstructured":"Kalyani G, Janakiramaiah B, Karuna A, Prasad L (2021) Diabetic retinopathy detection and classification using capsule networks. Complex Intell Syst. https:\/\/doi.org\/10.1007\/s40747-021-00318-9","DOI":"10.1007\/s40747-021-00318-9"},{"key":"3849_CR21","doi-asserted-by":"publisher","unstructured":"Dinani ST, Caragea D (2021) Disaster image classification using capsule networks. In: 2021 International Joint Conference on Neural Networks (IJCNN). https:\/\/doi.org\/10.1109\/IJCNN52387.2021.9534448, pp 1\u20138","DOI":"10.1109\/IJCNN52387.2021.9534448"},{"key":"3849_CR22","doi-asserted-by":"publisher","first-page":"9751","DOI":"10.1109\/ACCESS.2020.2964292","volume":"8","author":"J-T Hsu","year":"2020","unstructured":"Hsu J-T, Kuo C-H, Chen D-W (2020) Image super-resolution using capsule neural networks. IEEE Access 8:9751\u20139759. https:\/\/doi.org\/10.1109\/ACCESS.2020.2964292","journal-title":"IEEE Access"},{"key":"3849_CR23","doi-asserted-by":"publisher","first-page":"96920","DOI":"10.1109\/ACCESS.2020.2996282","volume":"8","author":"K Sun","year":"2020","unstructured":"Sun K, Yuan L, Xu H, Wen X (2020) Deep tensor capsule network. IEEE Access 8:96920\u201396933. https:\/\/doi.org\/10.1109\/ACCESS.2020.2996282","journal-title":"IEEE Access"},{"key":"3849_CR24","doi-asserted-by":"publisher","unstructured":"Gu J, Tresp V (2020) Improving the robustness of capsule networks to image affine transformations. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00731, pp 7283\u20137291","DOI":"10.1109\/CVPR42600.2020.00731"},{"issue":"12","key":"3849_CR25","doi-asserted-by":"publisher","first-page":"1850","DOI":"10.1109\/LSP.2018.2873892","volume":"25","author":"C Xiang","year":"2018","unstructured":"Xiang C, Zhang L, Tang Y, Zou W, Xu C (2018) Ms-capsnet: A novel multi-scale capsule network. IEEE Signal Process Lett 25(12):1850\u20131854. https:\/\/doi.org\/10.1109\/LSP.2018.2873892","journal-title":"IEEE Signal Process Lett"},{"issue":"43","key":"3849_CR26","doi-asserted-by":"publisher","first-page":"32243","DOI":"10.1007\/s11042-020-09455-8","volume":"79","author":"R Pucci","year":"2020","unstructured":"Pucci R, Micheloni C, Foresti G L, Martinel N (2020) Deep interactive encoding with capsule networks for image classification. Multimed Tools Appl 79(43):32243\u201332258. https:\/\/doi.org\/10.1007\/s11042-020-09455-8","journal-title":"Multimed Tools Appl"},{"issue":"10","key":"3849_CR27","doi-asserted-by":"publisher","first-page":"6927","DOI":"10.1007\/s00500-021-05774-6","volume":"25","author":"K Sun","year":"2021","unstructured":"Sun K, Wen X, Yuan L, Xu H (2021) Dense capsule networks with fewer parameters. Soft Comput 25(10):6927\u20136945. https:\/\/doi.org\/10.1007\/s00500-021-05774-6","journal-title":"Soft Comput"},{"issue":"3","key":"3849_CR28","doi-asserted-by":"publisher","first-page":"3066","DOI":"10.1007\/s10489-021-02630-w","volume":"52","author":"G Sun","year":"2022","unstructured":"Sun G, Ding S, Sun T, Zhang C, Du W (2022) A novel dense capsule network based on dense capsule layers. Appl Intell 52(3):3066\u20133076. https:\/\/doi.org\/10.1007\/s10489-021-02630-w","journal-title":"Appl Intell"},{"issue":"1","key":"3849_CR29","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1007\/s11063-020-10273-0","volume":"52","author":"M Amer","year":"2020","unstructured":"Amer M, Maul T (2020) Path capsule networks. Neural Process Lett 52(1):545\u2013559. https:\/\/doi.org\/10.1007\/s00500-021-05774-6","journal-title":"Neural Process Lett"},{"issue":"1","key":"3849_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-56847-4","volume":"10","author":"W Huang","year":"2020","unstructured":"Huang W, Zhou F (2020) Da-capsnet: dual attention mechanism capsule network. Sci Rep 10(1):1\u201313. https:\/\/doi.org\/10.1038\/s41598-020-68453-w","journal-title":"Sci Rep"},{"key":"3849_CR31","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.patrec.2021.01.017","volume":"144","author":"D Peer","year":"2021","unstructured":"Peer D, Stabinger S, Rodr\u00edguez-S\u00e1nchez A (2021) Limitation of capsule networks. Pattern Recog Lett 144:68\u201374. https:\/\/doi.org\/10.1016\/j.patrec.2021.01.017","journal-title":"Pattern Recog Lett"},{"key":"3849_CR32","doi-asserted-by":"publisher","unstructured":"Rajasegaran J, Jayasundara V, Jayasekara S, Jayasekara H, Seneviratne S, Rodrigo R (2019) Deepcaps: Going deeper with capsule networks. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https:\/\/doi.org\/10.1109\/CVPR.2019.01098, pp 10717\u201310725","DOI":"10.1109\/CVPR.2019.01098"},{"key":"3849_CR33","doi-asserted-by":"publisher","first-page":"85007","DOI":"10.1109\/ACCESS.2020.2992655","volume":"8","author":"S Yang","year":"2020","unstructured":"Yang S, Lee F, Miao R, Cai J, Chen L, Yao W, Kotani K, Chen Q (2020) Rs-capsnet: An advanced capsule network. IEEE Access 8:85007\u201385018. https:\/\/doi.org\/10.1109\/ACCESS.2020.2992655","journal-title":"IEEE Access"},{"key":"3849_CR34","doi-asserted-by":"publisher","unstructured":"Pucci R, Micheloni C, Martinel N (2021) Self-attention agreement among capsules. In: 2021 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW). https:\/\/doi.org\/10.1109\/ICCVW54120.2021.00035, pp 272\u2013280","DOI":"10.1109\/ICCVW54120.2021.00035"},{"issue":"1","key":"3849_CR35","doi-asserted-by":"publisher","first-page":"14634","DOI":"10.1038\/s41598-021-93977-0","volume":"11","author":"V Mazzia","year":"2021","unstructured":"Mazzia V, Salvetti F, Chiaberge M (2021) Efficient-CapsNet: capsule network with self-attention routing. Sci Rep 11(1):14634\u201314647. https:\/\/doi.org\/10.1038\/s41598-021-93977-0","journal-title":"Sci Rep"},{"key":"3849_CR36","doi-asserted-by":"publisher","first-page":"690","DOI":"10.1016\/j.neunet.2021.07.032","volume":"143","author":"Z Zhao","year":"2021","unstructured":"Zhao Z, Cheng S (2021) Capsule networks with non-iterative cluster routing. Neural Netw 143:690\u2013697. https:\/\/doi.org\/10.1016\/j.neunet.2021.07.032","journal-title":"Neural Netw"},{"key":"3849_CR37","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1016\/j.neunet.2021.06.018","volume":"143","author":"Y Li","year":"2021","unstructured":"Li Y, Zhao W, Cambria E, Wang S, Eger S (2021) Graph routing between capsules. Neural Netw 143:345\u2013354. https:\/\/doi.org\/10.1016\/j.neunet.2021.06.018","journal-title":"Neural Netw"},{"issue":"5","key":"3849_CR38","doi-asserted-by":"publisher","first-page":"815","DOI":"10.1162\/NECO_a_00824","volume":"28","author":"M Tygert","year":"2016","unstructured":"Tygert M, Bruna J, Chintala S, LeCun Y, Piantino S, Szlam A (2016) A mathematical motivation for complex-valued convolutional networks. Neural Comput 28(5):815\u2013825. https:\/\/doi.org\/10.1162\/neco_a_00824","journal-title":"Neural Comput"},{"key":"3849_CR39","doi-asserted-by":"publisher","unstructured":"Zhang H, Liu AQ (2021) An optical computing chip executing complex-valued neural network and its on-chip training. In: Katayama R, Takashima Y (eds) ODS 2021: industrial optical devices and systems. https:\/\/doi.org\/10.1117\/12.2597553. SPIE, pp 457\u2013468","DOI":"10.1117\/12.2597553"},{"issue":"3","key":"3849_CR40","doi-asserted-by":"publisher","first-page":"1395","DOI":"10.1007\/s11071-018-4134-0","volume":"92","author":"F Xu","year":"2018","unstructured":"Xu F, Zhang J, Fang T, Huang S, Wang M (2018) Synchronous dynamics in neural system coupled with memristive synapse. Nonlinear Dyn 92(3):1395\u20131402. https:\/\/doi.org\/10.1007\/s11071-018-4134-0","journal-title":"Nonlinear Dyn"},{"issue":"7","key":"3849_CR41","doi-asserted-by":"publisher","first-page":"074006","DOI":"10.1088\/1361-6579\/aace91","volume":"39","author":"PR Protachevicz","year":"2018","unstructured":"Protachevicz PR, Borges RR, Reis AS, Borges FS, Iarosz KC, Caldas IL, Lameu EL, Macau EEN, Viana RL, Sokolov IM, Ferrari FAS, Kurths J, Batista AM, Lo C-Y, He Y, Lin C-P (2018) Synchronous behaviour in network model based on human cortico-cortical connections. Physiol Meas 39(7):074006. https:\/\/doi.org\/10.1088\/1361-6579\/aace91","journal-title":"Physiol Meas"},{"issue":"17","key":"3849_CR42","doi-asserted-by":"publisher","first-page":"12062","DOI":"10.1021\/acs.analchem.0c02746","volume":"92","author":"Y Guo","year":"2020","unstructured":"Guo Y, Gao Z, Liu Y, Li S, Zhu J, Chen P, Liu B-F (2020) Multichannel synchronous hydrodynamic gating coupling with concentration gradient generator for high-throughput probing dynamic signaling of single cells. Anal Chem 92(17):12062\u201312070. https:\/\/doi.org\/10.1021\/acs.analchem.0c02746","journal-title":"Anal Chem"},{"key":"3849_CR43","doi-asserted-by":"publisher","first-page":"20293","DOI":"10.1109\/ACCESS.2019.2897000","volume":"7","author":"Q Yin","year":"2019","unstructured":"Yin Q, Wang J, Luo X, Zhai J, Jha SK, Shi Y-Q (2019) Quaternion convolutional neural network for color image classification and forensics. IEEE Access 7:20293\u201320301. https:\/\/doi.org\/10.1109\/ACCESS.2019.2897000","journal-title":"IEEE Access"},{"issue":"4","key":"3849_CR44","doi-asserted-by":"publisher","first-page":"2957","DOI":"10.1007\/s10462-019-09752-1","volume":"53","author":"T Parcollet","year":"2020","unstructured":"Parcollet T, Morchid M, Linar\u00e8s G (2020) A survey of quaternion neural networks. Artif Intell Rev 53(4):2957\u20132982. https:\/\/doi.org\/10.1007\/s10462-019-09752-1","journal-title":"Artif Intell Rev"},{"issue":"3","key":"3849_CR45","doi-asserted-by":"publisher","first-page":"949","DOI":"10.1007\/s11063-017-9716-1","volume":"47","author":"C-A Popa","year":"2018","unstructured":"Popa C-A (2018) Learning algorithms for quaternion-valued neural networks. Neural Process Lett 47(3):949\u2013973. https:\/\/doi.org\/10.1007\/s11063-017-9716-1","journal-title":"Neural Process Lett"},{"key":"3849_CR46","unstructured":"Zhang A, Tay Y, Zhang S, Chan A, Luu AT, Hui SC, Fu J (2021) Beyond fully-connected layers with quaternions: Parameterization of hypercomplex multiplications with 1\/n parameters 9Th international conference on learning representations, ICLR, pp 1\u201313"},{"key":"3849_CR47","unstructured":"Kosiorek A, Sabour S, Teh YW, Hinton GE (2019) Stacked capsule autoencoders. In: Advances in Neural Information Processing Systems, vol 32. https:\/\/proceedings.neurips.cc\/paper\/2019\/file\/2e0d41e02c5be4668ec1b0730b3346a8-Paper.pdf, pp 1\u201311"},{"key":"3849_CR48","doi-asserted-by":"publisher","unstructured":"Yang Y, Newsam S (2010) Bag-of-visual-words and spatial extensions for land-use classification. In: Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems - GIS\u201910. https:\/\/doi.org\/10.1145\/1869790.1869829, pp 270\u2013279","DOI":"10.1145\/1869790.1869829"},{"key":"3849_CR49","unstructured":"Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: Efficient convolutional neural networks for mobile vision applications Preprint at arXiv:1704.04861"},{"key":"3849_CR50","doi-asserted-by":"publisher","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: 2018 IEEE\/CVF conference on computer vision and pattern recognition. https:\/\/doi.org\/10.1109\/CVPR.2018.00474, pp 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"key":"3849_CR51","doi-asserted-by":"publisher","first-page":"107744","DOI":"10.1016\/j.patcog.2020.107744","volume":"112","author":"K-K Huang","year":"2021","unstructured":"Huang K-K, Ren C-X, Liu H, Lai Z-R, Yu Y-F, Dai D-Q (2021) Hyperspectral image classification via discriminative convolutional neural network with an improved triplet loss. Pattern Recog 112:107744\u2013107757. https:\/\/doi.org\/10.1016\/j.patcog.2020.107744","journal-title":"Pattern Recog"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03849-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-03849-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03849-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T02:08:54Z","timestamp":1675994934000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-03849-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,29]]},"references-count":51,"alternative-id":["3849"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-03849-x","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2022,6,29]]},"assertion":[{"value":"2 June 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 June 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}