{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T02:21:21Z","timestamp":1777947681239,"version":"3.51.4"},"reference-count":86,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,6,16]],"date-time":"2020-06-16T00:00:00Z","timestamp":1592265600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,6,16]],"date-time":"2020-06-16T00:00:00Z","timestamp":1592265600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Image Video Proc."],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This work presents a shallow network based on subspaces with applications in image classification. Recently, shallow networks based on PCA filter banks have been employed to solve many computer vision-related problems including texture classification, face recognition, and scene understanding. These approaches are robust, with a straightforward implementation that enables fast prototyping of practical applications. However, these architectures employ either unsupervised or supervised learning. As a result, they may not achieve highly discriminative features in more complicated computer vision problems containing variations in camera motion, object\u2019s appearance, pose, scale, and texture, due to drawbacks related to each learning paradigm. To cope with this disadvantage, we propose a semi-supervised shallow network equipped with both unsupervised and supervised filter banks, presenting representative and discriminative abilities. Besides, the introduced architecture is flexible, performing favorably on different applications whose amount of supervised data is an issue, making it an attractive choice in practice. The proposed network is evaluated on five datasets. The results show improvement in terms of prediction rate, comparing to current shallow networks.<\/jats:p>","DOI":"10.1186\/s13640-020-00507-5","type":"journal-article","created":{"date-parts":[[2020,6,16]],"date-time":"2020-06-16T12:02:36Z","timestamp":1592308956000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A semi-supervised convolutional neural network based on subspace representation for image classification"],"prefix":"10.1186","volume":"2020","author":[{"given":"Bernardo B.","family":"Gatto","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lincon S.","family":"Souza","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eulanda M.","family":"dos Santos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kazuhiro","family":"Fukui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Waldir S.","family":"S. J\u00fanior","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kenny V.","family":"dos Santos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,6,16]]},"reference":[{"issue":"1","key":"507_CR1","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1109\/TGRS.2017.2748120","volume":"56","author":"Z. Gong","year":"2018","unstructured":"Z. Gong, P. Zhong, Y. Yu, W. Hu, Diversity-promoting deep structural metric learning for remote sensing scene classification. IEEE Trans. Geosci. Remote Sens.56(1), 371\u2013390 (2018).","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"5","key":"507_CR2","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.1109\/TMI.2016.2535302","volume":"35","author":"N. Tajbakhsh","year":"2016","unstructured":"N. Tajbakhsh, J. Y. Shin, S. R. Gurudu, R. T. Hurst, C. B. Kendall, M. B. Gotway, J. Liang, Convolutional neural networks for medical image analysis: Full training or fine tuning?IEEE Trans. Med. Imaging. 35(5), 1299\u20131312 (2016).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"507_CR3","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1016\/j.patcog.2016.07.026","volume":"61","author":"A. T. Lopes","year":"2017","unstructured":"A. T. Lopes, E. de Aguiar, A. F. De Souza, T. Oliveira-Santos, Facial expression recognition with convolutional neural networks: coping with few data and the training sample order. Pattern Recog.61:, 610\u2013628 (2017).","journal-title":"Pattern Recog."},{"issue":"1","key":"507_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10514-015-9516-2","volume":"41","author":"X. Gao","year":"2017","unstructured":"X. Gao, T. Zhang, Unsupervised learning to detect loops using deep neural networks for visual slam system. Auton. Robot.41(1), 1\u201318 (2017).","journal-title":"Auton. Robot."},{"key":"507_CR5","doi-asserted-by":"crossref","unstructured":"X. Xie, H. Liu, M. Edmonds, F. Gaol, S. Qi, Y. Zhu, B. Rothrock, S. C. Zhu, in 2018 IEEE International Conference on Robotics and Automation (ICRA). Unsupervised learning of hierarchical models for hand-object interactions (IEEE, 2018), pp. 1\u20139.","DOI":"10.1109\/ICRA.2018.8461214"},{"key":"507_CR6","unstructured":"A. M. Dai, Q. V. Le, in Advances in neural information processing systems. Semi-supervised sequence learning, (2015), pp. 3079\u20133087."},{"key":"507_CR7","unstructured":"A. Dosovitskiy, J. T. Springenberg, M. Riedmiller, T. Brox, in Advances in Neural Information Processing Systems. Discriminative unsupervised feature learning with convolutional neural networks, (2014), pp. 766\u2013774."},{"issue":"2","key":"507_CR8","doi-asserted-by":"publisher","first-page":"115","DOI":"10.3233\/ICA-150505","volume":"23","author":"I. Bougoudis","year":"2016","unstructured":"I. Bougoudis, K. Demertzis, L. Iliadis, Fast and low cost prediction of extreme air pollution values with hybrid unsupervised learning. Integr. Comput. Aided Eng.23(2), 115\u2013127 (2016).","journal-title":"Integr. Comput. Aided Eng."},{"key":"507_CR9","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.jprocont.2017.02.006","volume":"67","author":"M. C. Thomas","year":"2018","unstructured":"M. C. Thomas, W. Zhu, J. A. Romagnoli, Data mining and clustering in chemical process databases for monitoring and knowledge discovery. J. Process Control. 67:, 160\u2013175 (2018).","journal-title":"J. Process Control"},{"issue":"1","key":"507_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-014-0007-7","volume":"2","author":"M. M. Najafabadi","year":"2015","unstructured":"M. M. Najafabadi, F. Villanustre, T. M. Khoshgoftaar, N. Seliya, R. Wald, E. Muharemagic, Deep learning applications and challenges in big data analytics. J. Big Data. 2(1), 1 (2015).","journal-title":"J. Big Data"},{"issue":"1","key":"507_CR11","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1109\/TSC.2015.2449302","volume":"9","author":"Q. Zhang","year":"2016","unstructured":"Q. Zhang, L. T. Yang, Z. Chen, Deep computation model for unsupervised feature learning on big data. IEEE Trans. Serv. Comput.9(1), 161\u2013171 (2016).","journal-title":"IEEE Trans. Serv. Comput."},{"key":"507_CR12","unstructured":"A. M. Dai, Q. V. Le, in Advances in neural information processing systems. Semi-supervised sequence learning, (2015), pp. 3079\u20133087."},{"key":"507_CR13","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1126\/science.aaa8415","volume":"349","author":"M. I. Jordan","year":"2015","unstructured":"M. I. Jordan, T. M. Mitchell, Machine learning: trends, perspectives, and prospects. Science. 349:, 255\u2013260 (2015).","journal-title":"Science"},{"key":"507_CR14","unstructured":"J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, T. Darrell, in International conference on machine learning. Decaf: a deep convolutional activation feature for generic visual recognition, (2014), pp. 647\u2013655."},{"key":"507_CR15","unstructured":"T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, X. Chen, in Advances in Neural Information Processing Systems. Improved techniques for training gans, (2016), pp. 2234\u20132242."},{"issue":"2","key":"507_CR16","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1007\/s40708-016-0042-6","volume":"3","author":"A. Holzinger","year":"2016","unstructured":"A. Holzinger, Interactive machine learning for health informatics: when do we need the human-in-the-loop?Brain Inf.3(2), 119\u2013131 (2016).","journal-title":"Brain Inf."},{"issue":"1","key":"507_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-012-9356-9","volume":"43","author":"S. S. Rautaray","year":"2015","unstructured":"S. S. Rautaray, A. Agrawal, Vision based hand gesture recognition for human computer interaction: a survey. Artif. Intell. Rev.43(1), 1\u201354 (2015).","journal-title":"Artif. Intell. Rev."},{"key":"507_CR18","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/j.patcog.2017.03.021","volume":"75","author":"J. Song","year":"2018","unstructured":"J. Song, L. Gao, L. Liu, X. Zhu, N. Sebe, Quantization-based hashing: a general framework for scalable image and video retrieval. Pattern Recog.75:, 175\u2013187 (2018).","journal-title":"Pattern Recog."},{"key":"507_CR19","doi-asserted-by":"crossref","unstructured":"R. Xia, Y. Pan, H. Lai, C. Liu, S. Yan, in AAAI. Supervised hashing for image retrieval via image representation learning, (2014), p. 2.","DOI":"10.1609\/aaai.v28i1.8952"},{"key":"507_CR20","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.cviu.2013.11.009","volume":"122","author":"T. Bouwmans","year":"2014","unstructured":"T. Bouwmans, E. H. Zahzah, Robust PCA via principal component pursuit: a review for a comparative evaluation in video surveillance. Comput Vision Image Underst.122:, 22\u201334 (2014).","journal-title":"Comput Vision Image Underst."},{"key":"507_CR21","doi-asserted-by":"crossref","unstructured":"S. Ojha, S. Sakhare, in Pervasive Computing (ICPC), 2015 International Conference on. Image processing techniques for object tracking in video surveillance-a survey (IEEE, 2015), pp. 1\u20136.","DOI":"10.1109\/PERVASIVE.2015.7087180"},{"key":"507_CR22","doi-asserted-by":"crossref","unstructured":"K. Jaseena, B. C. Kovoor, A survey on deep learning techniques for big data in biometrics. Int. J. Adv. Res. Comput. Sci.9(1) (2018).","DOI":"10.26483\/ijarcs.v9i1.5136"},{"issue":"3","key":"507_CR23","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1145\/3190618","volume":"51","author":"K. Sundararajan","year":"2018","unstructured":"K. Sundararajan, D. L. Woodard, Deep learning for biometrics: a survey. ACM Comput. Surv. (CSUR). 51(3), 65 (2018).","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"507_CR24","doi-asserted-by":"crossref","unstructured":"X. Geng, H. Zhang, J. Bian, T. S. Chua, in Proceedings of the IEEE International Conference on Computer Vision. Learning image and user features for recommendation in social networks, (2015), pp. 4274\u20134282.","DOI":"10.1109\/ICCV.2015.486"},{"key":"507_CR25","doi-asserted-by":"crossref","unstructured":"J. Wang, M. Korayem, S. Blanco, D. J. Crandall, in Proceedings of the 2016 ACM on Multimedia Conference. Tracking natural events through social media and computer vision (ACM, 2016), pp. 1097\u20131101.","DOI":"10.1145\/2964284.2984067"},{"key":"507_CR26","doi-asserted-by":"crossref","unstructured":"D. Ciregan, U. Meier, J. Schmidhuber, in Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. Multi-column deep neural networks for image classification (IEEE, 2012), pp. 3642\u20133649.","DOI":"10.1109\/CVPR.2012.6248110"},{"issue":"8","key":"507_CR27","doi-asserted-by":"publisher","first-page":"1915","DOI":"10.1109\/TPAMI.2012.231","volume":"35","author":"C. Farabet","year":"2013","unstructured":"C. Farabet, C. Couprie, L. Najman, Y. LeCun, Learning hierarchical features for scene labeling. IEEE Trans. Pattern Anal. Mach. Intell.35(8), 1915\u20131929 (2013).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"507_CR28","unstructured":"Y. Sun, Y. Chen, X. Wang, X. Tang, in Advances in Neural Information Processing Systems. Deep learning face representation by joint identification-verification, (2014), pp. 1988\u20131996."},{"key":"507_CR29","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.patcog.2017.05.025","volume":"71","author":"L. Nanni","year":"2017","unstructured":"L. Nanni, S. Ghidoni, S. Brahnam, Handcrafted vs. non-handcrafted features for computer vision classification. Pattern Recogn.71:, 158\u2013172 (2017).","journal-title":"Pattern Recogn."},{"key":"507_CR30","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.imavis.2016.06.007","volume":"55","author":"F. Zhu","year":"2016","unstructured":"F. Zhu, L. Shao, J. Xie, Y. Fang, From handcrafted to learned representations for human action recognition: a survey. Image Vision Comput.55:, 42\u201352 (2016).","journal-title":"Image Vision Comput."},{"issue":"2-3","key":"507_CR31","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/BF00341922","volume":"55","author":"M. R. Turner","year":"1986","unstructured":"M. R. Turner, Texture discrimination by gabor functions. Biol. Cybern.55(2-3), 71\u201382 (1986).","journal-title":"Biol. Cybern."},{"issue":"1","key":"507_CR32","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/0031-3203(95)00067-4","volume":"29","author":"T. Ojala","year":"1996","unstructured":"T. Ojala, M. Pietik\u00e4inen, D. Harwood, A comparative study of texture measures with classification based on featured distributions. Pattern Recog.29(1), 51\u201359 (1996).","journal-title":"Pattern Recog."},{"issue":"7","key":"507_CR33","doi-asserted-by":"publisher","first-page":"971","DOI":"10.1109\/TPAMI.2002.1017623","volume":"24","author":"T. Ojala","year":"2002","unstructured":"T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell.24(7), 971\u2013987 (2002).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"507_CR34","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"D. G. Lowe","year":"2004","unstructured":"D. G. Lowe, Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis.60(2), 91\u2013110 (2004).","journal-title":"Int. J. Comput. Vis."},{"key":"507_CR35","doi-asserted-by":"crossref","unstructured":"N. Dalal, B. Triggs, in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), vol. 1. Histograms of oriented gradients for human detection (IEEE, 2005), pp. 886\u2013893.","DOI":"10.1109\/CVPR.2005.177"},{"key":"507_CR36","doi-asserted-by":"crossref","unstructured":"K. Lai, L. Bo, X. Ren, D. Fox, in Robotics and Automation (ICRA) 2011 IEEE International Conference on. A large-scale hierarchical multi-view RGB-D object dataset (IEEE, 2011), pp. 1817\u20131824.","DOI":"10.1109\/ICRA.2011.5980382"},{"key":"507_CR37","doi-asserted-by":"crossref","unstructured":"Q. Zhu, M. C. Yeh, K. T. Cheng, S. Avidan, in Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, vol. 2. Fast human detection using a cascade of histograms of oriented gradients (IEEE, 2006), pp. 1491\u20131498.","DOI":"10.1109\/CVPR.2006.119"},{"key":"507_CR38","unstructured":"A Krizhevsky, I Sutskever, G. E Hinton, in Advances in neural information processing systems. Imagenet classification with deep convolutional neural networks, (2012), pp. 1097\u20131105."},{"issue":"3","key":"507_CR39","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1109\/MNET.2016.7474340","volume":"30","author":"M. A. Alsheikh","year":"2016","unstructured":"M. A. Alsheikh, D. Niyato, S. Lin, H. P. Tan, Z. Han, Mobile big data analytics using deep learning and Apache Spark. IEEE Netw.30(3), 22\u201329 (2016).","journal-title":"IEEE Netw."},{"key":"507_CR40","doi-asserted-by":"crossref","unstructured":"Y. Qian, J. Dong, W. Wang, T. Tan, in Media Watermarking, Security, and Forensics 2015, vol. 9409. Deep learning for steganalysis via convolutional neural networks, (2015), p. International Society for Optics and Photonics.","DOI":"10.1117\/12.2083479"},{"issue":"12","key":"507_CR41","doi-asserted-by":"publisher","first-page":"5017","DOI":"10.1109\/TIP.2015.2475625","volume":"24","author":"T. H. Chan","year":"2015","unstructured":"T. H. Chan, K. Jia, S. Gao, J. Lu, Z. Zeng, Y. Ma, PCANet: a simple deep learning baseline for image classification?IEEE Trans. Image Process.24(12), 5017\u20135032 (2015).","journal-title":"IEEE Trans. Image Process."},{"key":"507_CR42","unstructured":"M. Dorfer, R. Kelz, G. Widmer, Deep linear discriminant analysis. arXiv preprint arXiv:1511.04707 (2015)."},{"key":"507_CR43","doi-asserted-by":"crossref","unstructured":"C. Y. Low, A. B. J. Teoh, C. J. Ng, in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Multi-fold Gabor filter convolution descriptor for face recognition (IEEE, 2016), pp. 2094\u20132098.","DOI":"10.1109\/ICASSP.2016.7472046"},{"issue":"11","key":"507_CR44","doi-asserted-by":"publisher","first-page":"2164","DOI":"10.1109\/TPAMI.2015.2408358","volume":"37","author":"K. Fukui","year":"2015","unstructured":"K. Fukui, A. Maki, Difference subspace and its generalization for subspace-based methods. IEEE transactions on pattern analysis and machine intelligence. 37(11), 2164\u20132177 (2015).","journal-title":"IEEE transactions on pattern analysis and machine intelligence"},{"key":"507_CR45","doi-asserted-by":"crossref","unstructured":"M. Nishiyama, O. Yamaguchi, K. Fukui, in International Conference on Audio-and Video-Based Biometric Person Authentication. Face recognition with the multiple constrained mutual subspace method (Springer, 2005), pp. 71\u201380.","DOI":"10.1007\/11527923_8"},{"issue":"1","key":"507_CR46","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1007\/s12559-017-9489-x","volume":"10","author":"S. Ding","year":"2018","unstructured":"S. Ding, X. Xi, Z. Liu, H. Qiao, B. Zhang, A novel manifold regularized online semi-supervised learning model. Cogn. Comput.10(1), 49\u201361 (2018).","journal-title":"Cogn. Comput."},{"issue":"5","key":"507_CR47","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1145\/3191513","volume":"61","author":"T. Mitchell","year":"2018","unstructured":"T. Mitchell, W. Cohen, E. Hruschka, P. Talukdar, B. Yang, J. Betteridge, A. Carlson, B. Dalvi, M. Gardner, B. Kisiel, et al., Never-ending learning. Communications of the ACM. 61(5), 103\u2013115 (2018).","journal-title":"Communications of the ACM"},{"key":"507_CR48","unstructured":"C. J. Ng, A. B. J. Teoh, in 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA). Dctnet: a simple learning-free approach for face recognition (IEEE, 2015), pp. 761\u2013768."},{"issue":"11","key":"507_CR49","doi-asserted-by":"publisher","first-page":"4024","DOI":"10.3390\/s18114024","volume":"18","author":"J. N. Lee","year":"2018","unstructured":"J. N. Lee, Y. H. Byeon, S. B. Pan, K. C. Kwak, An EigenECG network approach based on PCANet for personal identification from ECG signal. Sensors. 18(11), 4024 (2018).","journal-title":"Sensors"},{"issue":"11","key":"507_CR50","doi-asserted-by":"publisher","first-page":"278","DOI":"10.3390\/info9110278","volume":"9","author":"T. Almeida","year":"2018","unstructured":"T. Almeida, H. Macedo, L. Matos, N. Vasconcelos, Prototyping a traffic light recognition device with expert knowledge. Information. 9(11), 278 (2018).","journal-title":"Information"},{"issue":"6","key":"507_CR51","doi-asserted-by":"publisher","first-page":"877","DOI":"10.3390\/rs10060877","volume":"10","author":"Y. Zi","year":"2018","unstructured":"Y. Zi, F. Xie, Z. Jiang, A cloud detection method for Landsat 8 images based on PCANet. Remote Sens.10(6), 877 (2018).","journal-title":"Remote Sens."},{"issue":"5","key":"507_CR52","doi-asserted-by":"publisher","first-page":"1477","DOI":"10.3390\/s18051477","volume":"18","author":"X. Zhu","year":"2018","unstructured":"X. Zhu, M. Ding, T. Huang, X. Jin, X. Zhang, PCANet-based structural representation for nonrigid multimodal medical image registration. Sensors. 18(5), 1477 (2018).","journal-title":"Sensors"},{"issue":"1","key":"507_CR53","doi-asserted-by":"publisher","first-page":"47","DOI":"10.3390\/rs11010047","volume":"11","author":"N. Wang","year":"2018","unstructured":"N. Wang, B. Li, Q. Xu, Y. Wang, Automatic ship detection in optical remote sensing images based on anomaly detection and SPP-PCANet. Remote Sens.11(1), 47 (2018). https:\/\/doi.org\/10.3390\/rs11010047.","journal-title":"Remote Sens."},{"key":"507_CR54","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1016\/j.ins.2017.01.011","volume":"385","author":"X. Yang","year":"2017","unstructured":"X. Yang, W. Liu, D. Tao, J. Cheng, Canonical correlation analysis networks for two-view image recognition. Inf. Sci.385:, 338\u2013352 (2017).","journal-title":"Inf. Sci."},{"issue":"8","key":"507_CR55","doi-asserted-by":"publisher","first-page":"1872","DOI":"10.1109\/TPAMI.2012.230","volume":"35","author":"J. Bruna","year":"2013","unstructured":"J. Bruna, S. Mallat, Invariant scattering convolution networks. IEEE Trans. Pattern Anal. Mach. Intell.35(8), 1872\u20131886 (2013).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"507_CR56","unstructured":"E. Oyallon, S. Mallat, L. Sifre, Generic deep networks with wavelet scattering. arXiv preprint arXiv:1312.5940 (2013)."},{"key":"507_CR57","doi-asserted-by":"crossref","unstructured":"L. Sifre, S. Mallat, in Proceedings of the IEEE conference on computer vision and pattern recognition. Rotation, scaling and deformation invariant scattering for texture discrimination, (2013), pp. 1233\u20131240.","DOI":"10.1109\/CVPR.2013.163"},{"key":"507_CR58","doi-asserted-by":"crossref","unstructured":"B. B. Gatto, E. M. dos Santos, in Image Processing (ICIP) 2017 IEEE International Conference on. Discriminative canonical correlation analysis network for image classification (IEEE, 2017), pp. 4487\u20134491.","DOI":"10.1109\/ICIP.2017.8297131"},{"issue":"6","key":"507_CR59","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.1109\/TPAMI.2007.1037","volume":"29","author":"T. K. Kim","year":"2007","unstructured":"T. K. Kim, J. Kittler, R. Cipolla, Discriminative learning and recognition of image set classes using canonical correlations. IEEE Trans. Pattern Anal. Mach. Intell.29(6), 1005\u20131018 (2007).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"507_CR60","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1007\/s11263-010-0381-3","volume":"91","author":"T. K. Kim","year":"2011","unstructured":"T. K. Kim, B. Stenger, J. Kittler, R. Cipolla, Incremental linear discriminant analysis using sufficient spanning sets and its applications. Int. J. Comput. Vis.91(2), 216\u2013232 (2011).","journal-title":"Int. J. Comput. Vis."},{"key":"507_CR61","doi-asserted-by":"crossref","unstructured":"B. B. Gatto, E. M. dos Santos, K. Fukui, in Document Analysis and Recognition (ICDAR) 2017 14th IAPR International Conference on, vol. 1. Subspace-based convolutional network for handwritten character recognition (IEEE, 2017), pp. 1044\u20131049.","DOI":"10.1109\/ICDAR.2017.173"},{"key":"507_CR62","doi-asserted-by":"crossref","unstructured":"D. Cui, G. Zhang, W. Han, L. Lekamalage Chamara Kasun, K. Hu Huang, in Proceedings of the IEEE International Conference on Computer Vision Workshops. Compact feature representation for image classification using ELMs, (2017), pp. 1015\u20131022.","DOI":"10.1109\/ICCVW.2017.124"},{"key":"507_CR63","doi-asserted-by":"crossref","unstructured":"M. R. Mohammadnia-Qaraei, R. Monsefi, K. Ghiasi-Shirazi, Convolutional kernel networks based on a convex combination of cosine kernels. Pattern Recogn. Lett. (2018).","DOI":"10.1016\/j.patrec.2018.09.016"},{"key":"507_CR64","unstructured":"K. Fukui, N. Sogi, T. Kobayashi, J. H. Xue, A. Maki, Discriminant analysis based on projection onto generalized difference subspace. arXiv preprint arXiv:1910.13113 (2019)."},{"key":"507_CR65","doi-asserted-by":"crossref","unstructured":"Y. Sun, L. Zheng, W. Deng, S. Wang, in Computer Vision (ICCV) 2017 IEEE International Conference on. SVDNet for pedestrian retrieval (IEEE, 2017), pp. 3820\u20133828.","DOI":"10.1109\/ICCV.2017.410"},{"issue":"10","key":"507_CR66","doi-asserted-by":"publisher","first-page":"5832","DOI":"10.1109\/TGRS.2016.2572736","volume":"54","author":"Z. Zou","year":"2016","unstructured":"Z. Zou, Z. Shi, Ship detection in spaceborne optical image with SVD networks. IEEE Trans. Geosci. Remote Sens.54(10), 5832\u20135845 (2016).","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"507_CR67","doi-asserted-by":"crossref","unstructured":"K. C. Lee, J. Ho, D. J. Kriegman, Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans. Pattern Anal. Mach. Intell., 684\u2013698 (2005).","DOI":"10.1109\/TPAMI.2005.92"},{"key":"507_CR68","doi-asserted-by":"crossref","unstructured":"Z. Q. Zhao, S. T. Xu, D. Liu, W. D. Tian, Z. D. Jiang, A review of image set classification. Neurocomputing (2018).","DOI":"10.1016\/j.neucom.2018.09.090"},{"key":"507_CR69","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cviu.2017.03.004","volume":"160","author":"L Chen","year":"2017","unstructured":"L Chen, N Hassanpour, Survey: How good are the current advances in image set based face identification?\u2013Experiments on three popular benchmarks with a na\u00efve approach. Comput. Vis. Image Underst.160:, 1\u201323 (2017).","journal-title":"Comput. Vis. Image Underst."},{"key":"507_CR70","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1016\/j.patcog.2017.11.020","volume":"76","author":"H. Tan","year":"2018","unstructured":"H. Tan, Y. Gao, Z. Ma, Regularized constraint subspace based method for image set classification. Pattern Recogn.76:, 434\u2013448 (2018).","journal-title":"Pattern Recogn."},{"key":"507_CR71","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.patcog.2017.05.025","volume":"71","author":"L. Nanni","year":"2017","unstructured":"L. Nanni, S. Ghidoni, S. Brahnam, Handcrafted vs. non-handcrafted features for computer vision classification. Pattern Recogn.71:, 158\u2013172 (2017).","journal-title":"Pattern Recogn."},{"key":"507_CR72","doi-asserted-by":"publisher","first-page":"596","DOI":"10.1016\/j.jvcir.2018.07.009","volume":"55","author":"S. Wazarkar","year":"2018","unstructured":"S. Wazarkar, B. N. Keshavamurthy, A survey on image data analysis through clustering techniques for real world applications. J. Visual Commun. Image Represent.55:, 596\u2013626 (2018).","journal-title":"J. Visual Commun. Image Represent."},{"key":"507_CR73","unstructured":"A. Krizhevsky, Learning multiple layers of features from tiny images. Master\u2019s thesis (University of Tront, 2009)."},{"key":"507_CR74","volume-title":"Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Tech. rep., Technical Report 07-49","author":"G. B. Huang","year":"2007","unstructured":"G. B. Huang, M. Ramesh, T. Berg, E. Learned-Miller, Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Tech. rep., Technical Report 07-49 (University of Massachusetts, Amherst, 2007)."},{"key":"507_CR75","doi-asserted-by":"crossref","unstructured":"N. Silberman, R. Fergus, in Computer Vision Workshops (ICCV Workshops) 2011 IEEE International Conference on. Indoor scene segmentation using a structured light sensor (IEEE, 2011), pp. 601\u2013608.","DOI":"10.1109\/ICCVW.2011.6130298"},{"key":"507_CR76","doi-asserted-by":"crossref","unstructured":"B. Leibe, B. Schiele, in Computer Vision and Pattern Recognition, 2003. Proceedings 2003 IEEE Computer Society Conference on, vol. 2. Analyzing appearance and contour based methods for object categorization (IEEE, 2003), pp. II\u2013409.","DOI":"10.1109\/CVPR.2003.1211497"},{"issue":"10","key":"507_CR77","doi-asserted-by":"publisher","first-page":"1090","DOI":"10.1109\/34.879790","volume":"22","author":"P. J. Phillips","year":"2000","unstructured":"P. J. Phillips, H. Moon, S. A. Rizvi, P. J. Rauss, The FERET evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1090\u20131104 (2000).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"issue":"1","key":"507_CR78","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1023\/B:VISI.0000042993.50813.60","volume":"61","author":"J. M. Geusebroek","year":"2005","unstructured":"J. M. Geusebroek, G. J. Burghouts, A. W. Smeulders, The Amsterdam library of object images. Int. J. Comput. Vis.61(1), 103\u2013112 (2005).","journal-title":"Int. J. Comput. Vis."},{"key":"507_CR79","doi-asserted-by":"crossref","unstructured":"I. Borg, P. J. Groenen, P. Mair, Applied multidimensional scaling and unfolding (Springer, 2017).","DOI":"10.1007\/978-3-319-73471-2"},{"key":"507_CR80","doi-asserted-by":"crossref","unstructured":"G. Huang, Z. Liu, L. Van Der Maaten, K. Q. Weinberger, in CVPR. Densely connected convolutional networks, (2017).","DOI":"10.1109\/CVPR.2017.243"},{"issue":"10","key":"507_CR81","doi-asserted-by":"publisher","first-page":"1914","DOI":"10.1109\/TASLP.2017.2729024","volume":"25","author":"C. T. Chung","year":"2017","unstructured":"C. T. Chung, C. Y. Tsai, C. H. Liu, L. S. Lee, Unsupervised iterative deep learning of speech features and acoustic tokens with applications to spoken term detection. IEEE\/ACM Trans. Audio Speech Lang. Process.25(10), 1914\u20131928 (2017).","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"507_CR82","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren, J. Sun, in Proceedings of the IEEE conference on computer vision and pattern recognition. Deep residual learning for image recognition, (2016), pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"507_CR83","doi-asserted-by":"crossref","unstructured":"L. Bertinetto, J. Valmadre, J. F. Henriques, A. Vedaldi, P. H. Torr, in European conference on computer vision. Fully-convolutional siamese networks for object tracking (Springer, 2016), pp. 850\u2013865.","DOI":"10.1007\/978-3-319-48881-3_56"},{"key":"507_CR84","doi-asserted-by":"crossref","unstructured":"R. R. Varior, M. Haloi, G. Wang, in European Conference on Computer Vision. Gated Siamese convolutional neural network architecture for human re-identification (Springer, 2016), pp. 791\u2013808.","DOI":"10.1007\/978-3-319-46484-8_48"},{"key":"507_CR85","doi-asserted-by":"crossref","unstructured":"C. Feichtenhofer, A. Pinz, A. Zisserman, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Convolutional two-stream network fusion for video action recognition, (2016), pp. 1933\u20131941.","DOI":"10.1109\/CVPR.2016.213"},{"key":"507_CR86","doi-asserted-by":"crossref","unstructured":"X. Peng, C. Schmid, in European Conference on Computer Vision. Multi-region two-stream R-CNN for action detection (Springer, 2016), pp. 744\u2013759.","DOI":"10.1007\/978-3-319-46493-0_45"}],"container-title":["EURASIP Journal on Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13640-020-00507-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13640-020-00507-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13640-020-00507-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T19:09:36Z","timestamp":1723057776000},"score":1,"resource":{"primary":{"URL":"https:\/\/jivp-eurasipjournals.springeropen.com\/articles\/10.1186\/s13640-020-00507-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,16]]},"references-count":86,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["507"],"URL":"https:\/\/doi.org\/10.1186\/s13640-020-00507-5","relation":{},"ISSN":["1687-5281"],"issn-type":[{"value":"1687-5281","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,16]]},"assertion":[{"value":"1 April 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 April 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 June 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"22"}}