{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T10:40:02Z","timestamp":1762252802139,"version":"3.37.3"},"reference-count":90,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2018,8,21]],"date-time":"2018-08-21T00:00:00Z","timestamp":1534809600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["Grant No. 61072110"],"award-info":[{"award-number":["Grant No. 61072110"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shaanxi Province key project of Research and Development Plan","award":["S2018-YF-ZDGY-0187"],"award-info":[{"award-number":["S2018-YF-ZDGY-0187"]}]},{"name":"International Cooperation Project of Shaanxi Province","award":["S2018-YF-GHMS-0061"],"award-info":[{"award-number":["S2018-YF-GHMS-0061"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2020,5]]},"DOI":"10.1007\/s00521-018-3685-9","type":"journal-article","created":{"date-parts":[[2018,8,21]],"date-time":"2018-08-21T02:44:33Z","timestamp":1534819473000},"page":"4481-4505","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["NMF with local constraint and Deep NMF with temporal dependencies constraint for action recognition"],"prefix":"10.1007","volume":"32","author":[{"given":"Ming","family":"Tong","sequence":"first","affiliation":[]},{"given":"Yiran","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Ma","sequence":"additional","affiliation":[]},{"given":"He","family":"Bai","sequence":"additional","affiliation":[]},{"given":"Xing","family":"Yue","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,8,21]]},"reference":[{"issue":"7","key":"3685_CR1","doi-asserted-by":"crossref","first-page":"2047","DOI":"10.1007\/s00521-015-2002-0","volume":"27","author":"Z Gao","year":"2016","unstructured":"Gao Z, Zhang H, Liu AA, Xu G, Xue Y (2016) Human action recognition on depth dataset. Neural Comput Appl 27(7):2047\u20132054","journal-title":"Neural Comput Appl"},{"key":"3685_CR2","doi-asserted-by":"crossref","unstructured":"Laptev I, Marszalek M, Schmid C, Rozenfeld B (2008) Learning realistic human actions from movies. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 1\u20138","DOI":"10.1109\/CVPR.2008.4587756"},{"issue":"3","key":"3685_CR3","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1007\/s11263-015-0846-5","volume":"119","author":"H Wang","year":"2016","unstructured":"Wang H, Oneata D, Verbeek J, Schmid C (2016) A robust and efficient video representation for action recognition. Int J Comput Vis 119(3):219\u2013238","journal-title":"Int J Comput Vis"},{"key":"3685_CR4","doi-asserted-by":"crossref","unstructured":"Caetano C, dos Santos JA, Schwartz WR (2016) Optical flow co-occurrence matrices: a novel spatiotemporal feature descriptor. In: Proceedings of the international conference on pattern recognition (ICPR), pp 1947\u20131952","DOI":"10.1109\/ICPR.2016.7899921"},{"issue":"3","key":"3685_CR5","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1109\/TCSVT.2016.2637778","volume":"27","author":"RVHM Colque","year":"2017","unstructured":"Colque RVHM, Caetano C, de Andrade MTL, Schwartz WR (2017) Histograms of optical flow orientation and magnitude and entropy to detect anomalous events in videos. IEEE Trans Circuits Syst Video Technol 27(3):673\u2013682","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"3685_CR6","doi-asserted-by":"crossref","unstructured":"Dollar P, Rabaud V, Cottrell G, Belongie S (2005) Behavior recognition via sparse spatio-temporal features. In: IEEE international workshop on visual surveillance and performance evaluation of tracking and surveillance, pp 65\u201372","DOI":"10.1109\/VSPETS.2005.1570899"},{"issue":"1","key":"3685_CR7","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1007\/s11263-012-0594-8","volume":"103","author":"H Wang","year":"2013","unstructured":"Wang H, Kl\u00e4ser A, Schmid C, Liu CL (2013) Dense trajectories and motion boundary descriptors for action recognition. Int J Comput Vis 103(1):60\u201379","journal-title":"Int J Comput Vis"},{"key":"3685_CR8","doi-asserted-by":"crossref","unstructured":"Sun L, Jia K, Chan TH, Fang Y, Wang G, Yan S (2014) DL-SFA: deeply-learned slow feature analysis for action recognition In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 2625\u20132632","DOI":"10.1109\/CVPR.2014.336"},{"key":"3685_CR9","unstructured":"Le QV, Zou WY, Yeung SY, Ng AY (2011) Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 3361\u20133368"},{"issue":"6755","key":"3685_CR10","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1038\/44565","volume":"401","author":"DD Lee","year":"1999","unstructured":"Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788\u2013791","journal-title":"Nature"},{"issue":"7","key":"3685_CR11","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","volume":"18","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527\u20131554","journal-title":"Neural Comput"},{"issue":"8","key":"3685_CR12","doi-asserted-by":"crossref","first-page":"3590","DOI":"10.1109\/TIP.2014.2331141","volume":"23","author":"S Taheri","year":"2014","unstructured":"Taheri S, Qiu Q, Chellappa R (2014) Structure-preserving sparse decomposition for facial expression analysis. IEEE Trans Image Process 23(8):3590\u20133603","journal-title":"IEEE Trans Image Process"},{"issue":"4","key":"3685_CR13","doi-asserted-by":"crossref","first-page":"1265","DOI":"10.1007\/s00521-016-2764-z","volume":"30","author":"Y Sun","year":"2018","unstructured":"Sun Y, Quan Y, Fu J (2018) Sparse coding and dictionary learning with class-specific group sparsity. Neural Comput Appl 30(4):1265\u20131275","journal-title":"Neural Comput Appl"},{"key":"3685_CR14","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1016\/j.neucom.2017.08.063","volume":"275","author":"X Wang","year":"2018","unstructured":"Wang X, Gao L, Song J, Zhen X, Sebe N, Shen HT (2018) Deep appearance and motion learning for egocentric activity recognition. Neurocomputing 275:438\u2013447","journal-title":"Neurocomputing"},{"issue":"1\u20133","key":"3685_CR15","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/0169-7439(87)80084-9","volume":"2","author":"S Wold","year":"1987","unstructured":"Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemom Intell Lab Syst 2(1\u20133):37\u201352","journal-title":"Chemom Intell Lab Syst"},{"issue":"1","key":"3685_CR16","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.jneumeth.2003.10.009","volume":"134","author":"A Delorme","year":"2004","unstructured":"Delorme A, Makeig S (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 134(1):9\u201321","journal-title":"J Neurosci Methods"},{"issue":"7","key":"3685_CR17","doi-asserted-by":"crossref","first-page":"1392","DOI":"10.1109\/TCSVT.2016.2539779","volume":"27","author":"Y Lu","year":"2017","unstructured":"Lu Y, Lai Z, Xu Y, Zhang D, Yuan C (2017) Nonnegative discriminant matrix factorization. IEEE Trans Circuits Syst Video Technol 27(7):1392\u20131405","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"9","key":"3685_CR18","doi-asserted-by":"crossref","first-page":"2148","DOI":"10.1109\/TNNLS.2014.2376963","volume":"26","author":"C Gong","year":"2015","unstructured":"Gong C, Tao D, Fu K, Yang J (2015) Fick\u2019s law assisted propagation for semisupervised learning. IEEE Trans Neural Netw Learn Syst 26(9):2148\u20132162","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"5","key":"3685_CR19","doi-asserted-by":"crossref","first-page":"1947","DOI":"10.1109\/TNNLS.2017.2691725","volume":"29","author":"Z Li","year":"2018","unstructured":"Li Z, Tang J, He X (2018) Robust structured nonnegative matrix factorization for image representation. IEEE Trans Neural Netw Learn Syst 29(5):1947\u20131960","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"9","key":"3685_CR20","doi-asserted-by":"crossref","first-page":"2269","DOI":"10.1109\/TSP.2016.2516971","volume":"64","author":"M Tepper","year":"2016","unstructured":"Tepper M, Sapiro G (2016) Compressed nonnegative matrix factorization is fast and accurate. IEEE Trans Signal Process 64(9):2269\u20132283","journal-title":"IEEE Trans Signal Process"},{"issue":"3","key":"3685_CR21","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1109\/TPAMI.2016.2554555","volume":"39","author":"G Trigeorgis","year":"2017","unstructured":"Trigeorgis G, Bousmalis K, Zafeiriou S, Schuller BW (2017) A deep matrix factorization method for learning attribute representations. IEEE Trans Pattern Anal Mach Intell 39(3):417\u2013429","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3685_CR22","doi-asserted-by":"crossref","unstructured":"Thurau C, Hlav\u00e1c V (2008) Pose primitive based human action recognition in videos or still images. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 1\u20138","DOI":"10.1109\/CVPR.2008.4587721"},{"key":"3685_CR23","doi-asserted-by":"crossref","unstructured":"Yang Y, Tu D, Li G (2014) Gait recognition using flow histogram energy image. In: Proceedings of the international conference on pattern recognition (ICPR), pp 444\u2013449","DOI":"10.1109\/ICPR.2014.85"},{"issue":"2","key":"3685_CR24","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1109\/TII.2016.2605629","volume":"13","author":"H Zhang","year":"2017","unstructured":"Zhang H, Cao X, Ho JKL, Chow TWS (2017) Object-level video advertising: an optimization framework. IEEE Trans Ind Inf 13(2):520\u2013531","journal-title":"IEEE Trans Ind Inf"},{"key":"3685_CR25","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.patcog.2015.11.022","volume":"53","author":"Y Yi","year":"2016","unstructured":"Yi Y, Lin M (2016) Human action recognition with graph-based multiple-instance learning. Pattern Recognit 53:148\u2013162","journal-title":"Pattern Recognit"},{"issue":"5","key":"3685_CR26","doi-asserted-by":"crossref","first-page":"1813","DOI":"10.1016\/j.patcog.2013.12.004","volume":"47","author":"J Cho","year":"2014","unstructured":"Cho J, Lee M, Chang HJ, Oh S (2014) Robust action recognition using local motion and group sparsity. Pattern Recognit 47(5):1813\u20131825","journal-title":"Pattern Recognit"},{"key":"3685_CR27","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.neunet.2014.11.007","volume":"63","author":"W Hu","year":"2015","unstructured":"Hu W, Choi KS, Wang P, Jiang Y, Wang S (2015) Convex nonnegative matrix factorization with manifold regularization. Neural Netw 63:94\u2013103","journal-title":"Neural Netw"},{"issue":"8","key":"3685_CR28","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1016\/j.patrec.2009.09.011","volume":"31","author":"AK Jain","year":"2010","unstructured":"Jain AK (2010) Data clustering: 50\u00a0years beyond K-means. Pattern Recognit Lett 31(8):651\u2013666","journal-title":"Pattern Recognit Lett"},{"issue":"1","key":"3685_CR29","first-page":"253","volume":"18","author":"E Arias-Castro","year":"2017","unstructured":"Arias-Castro E, Lerman G, Zhang T (2017) Spectral clustering based on local PCA. J Mach Learn Res 18(1):253\u2013309","journal-title":"J Mach Learn Res"},{"key":"3685_CR30","doi-asserted-by":"crossref","unstructured":"Tian Y, Ruan Q, An G, Liu R (2014) Local non-negative component representation for human action recognition. In: Proceedings of the IEEE international conference on signal processing (ICSP), pp 1317\u20131320","DOI":"10.1109\/ICOSP.2014.7015213"},{"key":"3685_CR31","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.neucom.2012.12.054","volume":"124","author":"C Vollmer","year":"2014","unstructured":"Vollmer C, Hellbach S, Eggert J, Gross HM (2014) Sparse coding of human motion trajectories with non-negative matrix factorization. Neurocomputing 124:22\u201332","journal-title":"Neurocomputing"},{"key":"3685_CR32","doi-asserted-by":"crossref","unstructured":"Zafeiriou L, Nikitidis S, Zafeiriou S, Pantic M (2014) Slow features nonnegative matrix factorization for temporal data decomposition. In: Proceedings of the IEEE international conference on image processing (ICIP), pp 1430\u20131434","DOI":"10.1109\/ICIP.2014.7025286"},{"issue":"12","key":"3685_CR33","doi-asserted-by":"crossref","first-page":"5603","DOI":"10.1109\/TIP.2017.2735186","volume":"26","author":"L Zafeiriou","year":"2017","unstructured":"Zafeiriou L, Panagakis Y, Pantic M, Zafeiriou S (2017) Nonnegative decompositions for dynamic visual data Analysis. IEEE Trans Image Process 26(12):5603\u20135617","journal-title":"IEEE Trans Image Process"},{"key":"3685_CR34","doi-asserted-by":"crossref","unstructured":"Xiao Q, Cheng J, Jiang J, Feng W (2014) Position-based action recognition using high dimension index tree. In: Proceedings of the international conference on pattern recognition (ICPR), pp 4400\u20134405","DOI":"10.1109\/ICPR.2014.753"},{"key":"3685_CR35","doi-asserted-by":"crossref","unstructured":"Ji X, Cheng J, Tao D (2015) Local mean spatio-temporal feature for depth image-based speed-up action recognition. In: Proceedings of the IEEE international conference on image processing (ICIP), pp 2389\u20132393","DOI":"10.1109\/ICIP.2015.7351230"},{"key":"3685_CR36","unstructured":"Roth PM, Mauthner T, Khan I, Bischof H (2009) Efficient human action recognition by cascaded linear classification. In: Proceedings of the IEEE international conference on computer vision workshops (ICCV Workshops), pp 546\u2013553"},{"issue":"2","key":"3685_CR37","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1109\/TIP.2013.2292550","volume":"23","author":"H Wang","year":"2014","unstructured":"Wang H, Yuan C, Hu W, Ling H, Yang W, Sun C (2014) Action recognition using nonnegative action component representation and sparse basis selection. IEEE Trans Image Process 23(2):570\u2013581","journal-title":"IEEE Trans Image Process"},{"issue":"4","key":"3685_CR38","doi-asserted-by":"crossref","first-page":"1259","DOI":"10.1587\/transinf.2015EDL8164","volume":"99","author":"J Wang","year":"2016","unstructured":"Wang J, Zhang P, Luo L (2016) Nonnegative component representation with hierarchical dictionary learning strategy for action recognition. IEICE Trans Inf Syst 99(4):1259\u20131263","journal-title":"IEICE Trans Inf Syst"},{"key":"3685_CR39","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.patrec.2015.06.029","volume":"65","author":"M Sekma","year":"2015","unstructured":"Sekma M, Mejdoub M, Amar CB (2015) Human action recognition based on multi-layer fisher vector encoding method. Pattern Recognit Lett 65:37\u201343","journal-title":"Pattern Recognit Lett"},{"key":"3685_CR40","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.patcog.2017.02.004","volume":"67","author":"YF Yu","year":"2017","unstructured":"Yu YF, Dai DQ, Ren CX, Hang KK (2017) Discriminative multi-layer illumination-robust feature extraction for face recognition. Pattern Recognit 67:201\u2013212","journal-title":"Pattern Recognit"},{"key":"3685_CR41","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-018-3579-x","author":"H Zhang","year":"2018","unstructured":"Zhang H, Ji Y, Huang W, Liu L (2018) Sitcom-star-based clothing retrieval for video advertising: a deep learning framework. Neural Comput Appl. \nhttps:\/\/doi.org\/10.1007\/s00521-018-3579-x","journal-title":"Neural Comput Appl"},{"key":"3685_CR42","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.sigpro.2015.09.038","volume":"124","author":"Q Liao","year":"2016","unstructured":"Liao Q, Zhang Q (2016) Local coordinate based graph-regularized NMF for image representation. Signal Process 124:103\u2013114","journal-title":"Signal Process"},{"key":"3685_CR43","doi-asserted-by":"crossref","unstructured":"Wang H, Schmid C (2013) Action recognition with improved trajectories. In: Proceedings of IEEE international conference on computer vision (ICCV), pp 3551\u20133558","DOI":"10.1109\/ICCV.2013.441"},{"key":"3685_CR44","volume-title":"Structured regularization for large vector autoregression","author":"WB Nicholson","year":"2014","unstructured":"Nicholson WB, Matteson DS, Bien J (2014) Structured regularization for large vector autoregression. Cornell University, Ithaca"},{"issue":"12","key":"3685_CR45","doi-asserted-by":"crossref","first-page":"2247","DOI":"10.1109\/TPAMI.2007.70711","volume":"29","author":"L Gorelick","year":"2007","unstructured":"Gorelick L, Blank M, Shechtman E, Irani M, Basri R (2007) Actions as space-time shapes. IEEE Trans Pattern Anal Mach Intell 29(12):2247\u20132253","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3685_CR46","doi-asserted-by":"crossref","unstructured":"Schuldt C, Laptev I, Caputo B (2004) Recognizing human actions: a local SVM approach. In: Proceedings of the international conference on pattern recognition (ICPR), pp 32\u201336","DOI":"10.1109\/ICPR.2004.1334462"},{"key":"3685_CR47","doi-asserted-by":"crossref","unstructured":"Rodriguez MD, Ahmed J, Shah M (2008) Action mach a spatio-temporal maximum average correlation height filter for action recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 1\u20138","DOI":"10.1109\/CVPR.2008.4587727"},{"key":"3685_CR48","doi-asserted-by":"crossref","unstructured":"Liu J, Luo J, Shah M (2009) Recognizing realistic actions from videos \u201cin the wild\u201d. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 1996\u20132003","DOI":"10.1109\/CVPR.2009.5206744"},{"issue":"2","key":"3685_CR49","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1007\/s10994-016-5553-0","volume":"103","author":"K Kimura","year":"2016","unstructured":"Kimura K, Kudo M, Tanaka Y (2016) A column-wise update algorithm for nonnegative matrix factorization in Bregman divergence with an orthogonal constraint. Mach Learn 103(2):285\u2013306","journal-title":"Mach Learn"},{"issue":"1","key":"3685_CR50","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1109\/TKDE.2016.2606098","volume":"29","author":"K Allab","year":"2017","unstructured":"Allab K, Labiod L, Nadif M (2017) A semi-NMF-PCA unified framework for data clustering. IEEE Trans Knowl Data Eng 29(1):2\u201316","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"3685_CR51","doi-asserted-by":"crossref","unstructured":"Zhang X, Yang Y, Jia H, Zhou H, Jiao L (2014) Low-rank representation based action recognition. In: Proceedings of the IEEE international joint conference on neural networks (IJCNN), pp 1812\u20131818","DOI":"10.1109\/IJCNN.2014.6889735"},{"key":"3685_CR52","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.neucom.2015.01.064","volume":"158","author":"B Sheng","year":"2015","unstructured":"Sheng B, Yang W, Sun C (2015) Action recognition using direction-dependent feature pairs and non-negative low rank sparse model. Neurocomputing 158:73\u201380","journal-title":"Neurocomputing"},{"issue":"4","key":"3685_CR53","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1109\/TPAMI.2015.2469288","volume":"38","author":"K Kulkarni","year":"2016","unstructured":"Kulkarni K, Turaga P (2016) Reconstruction-free action inference from compressive imagers. IEEE Trans Pattern Anal Mach Intell 38(4):772\u2013784","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"12","key":"3685_CR54","doi-asserted-by":"crossref","first-page":"2250","DOI":"10.1109\/TCSVT.2015.2502839","volume":"26","author":"DP Barrett","year":"2016","unstructured":"Barrett DP, Siskind JM (2016) Action recognition by time series of retinotopic appearance and motion features. IEEE Trans Circuits Syst Video Technol 26(12):2250\u20132263","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"3","key":"3685_CR55","doi-asserted-by":"crossref","first-page":"784","DOI":"10.1109\/TCYB.2016.2526970","volume":"47","author":"F Azhar","year":"2017","unstructured":"Azhar F, Li CT (2017) Hierarchical relaxed partitioning system for activity recognition. IEEE Trans Cybern 47(3):784\u2013795","journal-title":"IEEE Trans Cybern"},{"issue":"1","key":"3685_CR56","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","volume":"35","author":"S Ji","year":"2013","unstructured":"Ji S, Xu W, Yang M, Yu K (2013) 3D convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Mach Intell 35(1):221\u2013231","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3685_CR57","doi-asserted-by":"crossref","unstructured":"Umakanthan S, Denman S, Fookes C, Sridharan S (2014) Multiple instance dictionary learning for activity representation. In: Proceedings of the international conference on pattern recognition (ICPR), pp 1377\u20131382","DOI":"10.1109\/ICPR.2014.246"},{"key":"3685_CR58","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1016\/j.neucom.2014.04.090","volume":"151","author":"AA Liu","year":"2015","unstructured":"Liu AA, Xu N, Su YT, Hao T, Yang ZX (2015) Single\/multi-view human action recognition via regularized multi-task learning. Neurocomputing 151:544\u2013553","journal-title":"Neurocomputing"},{"key":"3685_CR59","doi-asserted-by":"crossref","unstructured":"Leyva R, Sanchez V, Li CT (2016) A fast binary pair-based video descriptor for action recognition. In: Proceedings of the IEEE international conference on image processing (ICIP), pp 4185\u20134189","DOI":"10.1109\/ICIP.2016.7533148"},{"key":"3685_CR60","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.neucom.2015.03.097","volume":"173","author":"F Baumann","year":"2016","unstructured":"Baumann F, Ehlers A, Rosenhahn B, Liao J (2016) Recognizing human actions using novel space-time volume binary patterns. Neurocomputing 173:54\u201363","journal-title":"Neurocomputing"},{"key":"3685_CR61","doi-asserted-by":"crossref","unstructured":"Lan T, Zhu Y, Roshan Zamir A, Savarse Silvio (2015) Action recognition by hierarchical mid-level action elements. In: Proceedings of the IEEE international conference on computer vision (ICCV), pp 4552\u20134560","DOI":"10.1109\/ICCV.2015.517"},{"issue":"2","key":"3685_CR62","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/s11263-015-0867-0","volume":"118","author":"C Yuan","year":"2016","unstructured":"Yuan C, Wu B, Li X, Hu W, Maybank S, Wang F (2016) Fusing R features and local features with context-aware kernels for action recognition. Int J Comput Vis 118(2):151\u2013171","journal-title":"Int J Comput Vis"},{"key":"3685_CR63","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.patcog.2016.11.012","volume":"64","author":"T Yao","year":"2017","unstructured":"Yao T, Wang Z, Xie Z, Gao J, Feng DD (2017) Learning universal multiview dictionary for human action recognition. Pattern Recognit 64:236\u2013244","journal-title":"Pattern Recognit"},{"issue":"4","key":"3685_CR64","doi-asserted-by":"crossref","first-page":"1748","DOI":"10.1109\/TIP.2017.2788196","volume":"27","author":"Y Tian","year":"2018","unstructured":"Tian Y, Kong Y, Ruan Q, An G, Fu Y (2018) Hierarchical and spatio-temporal sparse representation for human action recognition. IEEE Trans Image Process 27(4):1748\u20131762","journal-title":"IEEE Trans Image Process"},{"issue":"7","key":"3685_CR65","doi-asserted-by":"crossref","first-page":"1635","DOI":"10.1109\/TPAMI.2012.253","volume":"35","author":"Y Yang","year":"2013","unstructured":"Yang Y, Saleemi I, Shah M (2013) Discovering motion primitives for unsupervised grouping and one-shot learning of human actions, gestures, and expressions. IEEE Trans Pattern Anal Mach Intell 35(7):1635\u20131648","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"6","key":"3685_CR66","first-page":"1525","volume":"16","author":"S Samanta","year":"2014","unstructured":"Samanta S, Chanda B (2014) Space-time facet model for human activity classification. IEEE Trans Multimedia 16(6):1525\u20131535","journal-title":"IEEE Trans Multimedia"},{"key":"3685_CR67","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/j.neucom.2015.04.059","volume":"167","author":"Y Tian","year":"2015","unstructured":"Tian Y, Ruan Q, An G, Xu W (2015) Context and locality constrained linear coding for human action recognition. Neurocomputing 167:359\u2013370","journal-title":"Neurocomputing"},{"key":"3685_CR68","unstructured":"Chatzis SP, Kosmopoulos D (2015) A nonparametric bayesian approach toward stacked convolutional independent component analysis. In: Proceedings of the IEEE international conference on computer vision (ICCV), pp 2803\u20132811"},{"key":"3685_CR69","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neucom.2016.04.007","volume":"201","author":"T Zhou","year":"2016","unstructured":"Zhou T, Li N, Cheng X, Xu Q, Zhou L, Wu Z (2016) Learning semantic context feature-tree for action recognition via nearest neighbor fusion. Neurocomputing 201:1\u201311","journal-title":"Neurocomputing"},{"key":"3685_CR70","doi-asserted-by":"crossref","unstructured":"Tian Y, Sukthankar R, Shah M (2013) Spatiotemporal deformable part models for action detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 2642\u20132649","DOI":"10.1109\/CVPR.2013.341"},{"key":"3685_CR71","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.patcog.2016.05.010","volume":"60","author":"YP Hsu","year":"2016","unstructured":"Hsu YP, Liu C, Chen TY, Fu LC (2016) Online view-invariant human action recognition using rgb-d spatio-temporal matrix. Pattern Recognit 60:215\u2013226","journal-title":"Pattern Recognit"},{"key":"3685_CR72","unstructured":"Parisi GI, Wermter S (2017) Lifelong learning of action representations with deep neural self-organization. In: The AAAI 2017 spring symposium on science of intelligence: computational principles of natural and artificial intelligence, pp 608\u2013612"},{"issue":"7","key":"3685_CR73","doi-asserted-by":"crossref","first-page":"1769","DOI":"10.1109\/TCYB.2016.2558447","volume":"47","author":"M Rodriguez","year":"2017","unstructured":"Rodriguez M, Orrite C, Medrano C, Makris D (2017) One-shot learning of human activity with an map adapted GMM and simplex-HMM. IEEE Trans Cybern 47(7):1769\u20131780","journal-title":"IEEE Trans Cybern"},{"issue":"1","key":"3685_CR74","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1109\/TCYB.2015.2399172","volume":"46","author":"L Liu","year":"2016","unstructured":"Liu L, Shao L, Li X, Lu K (2016) Learning spatio-temporal representations for action recognition: a genetic programming approach. IEEE Trans Cybern 46(1):158\u2013170","journal-title":"IEEE Trans Cybern"},{"issue":"4","key":"3685_CR75","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1109\/TCSVT.2012.2225911","volume":"26","author":"H Cheng","year":"2016","unstructured":"Cheng H, Liu Z, Hou L, Yang J (2016) Sparsity-induced similarity measure and its applications. IEEE Trans Circuits Syst Video Technol 26(4):613\u2013626","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"2","key":"3685_CR76","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/s11263-015-0861-6","volume":"118","author":"L Shao","year":"2016","unstructured":"Shao L, Liu L, Yu M (2016) Kernelized multiview projection for robust action recognition. Int J Comput Vis 118(2):115\u2013129","journal-title":"Int J Comput Vis"},{"issue":"1","key":"3685_CR77","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1109\/TPAMI.2016.2537337","volume":"39","author":"AA Liu","year":"2017","unstructured":"Liu AA, Su YT, Nie WZ, Kankanhalli M (2017) Hierarchical clustering multi-task learning for joint human action grouping and recognition. IEEE Trans Pattern Anal Mach Intell 39(1):102\u2013114","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"7","key":"3685_CR78","doi-asserted-by":"crossref","first-page":"1510","DOI":"10.1109\/TMM.2017.2666540","volume":"19","author":"Y Shi","year":"2017","unstructured":"Shi Y, Tian Y, Wang Y, Huang T (2017) Sequential deep trajectory descriptor for action recognition with three-stream CNN. IEEE Trans Multimedia 19(7):1510\u20131520","journal-title":"IEEE Trans Multimedia"},{"issue":"4","key":"3685_CR79","first-page":"769","volume":"20","author":"S Zhang","year":"2018","unstructured":"Zhang S, Gao C, Zhang J, Chen F, Sang N (2018) Discriminative part selection for human action recognition. IEEE Trans Multimedia 20(4):769\u2013780","journal-title":"IEEE Trans Multimedia"},{"key":"3685_CR80","doi-asserted-by":"crossref","unstructured":"Byrne J (2015) Nested motion descriptors. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 502\u2013510","DOI":"10.1109\/CVPR.2015.7298648"},{"issue":"8","key":"3685_CR81","doi-asserted-by":"crossref","first-page":"2488","DOI":"10.1109\/TIP.2015.2424316","volume":"24","author":"C Sun","year":"2015","unstructured":"Sun C, Junejo IN, Tappen M, Foroosh H (2015) Exploring sparseness and self-similarity for action recognition. IEEE Trans Image Process 24(8):2488\u20132501","journal-title":"IEEE Trans Image Process"},{"issue":"1","key":"3685_CR82","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1109\/TCSVT.2014.2333151","volume":"25","author":"TV Nguyen","year":"2015","unstructured":"Nguyen TV, Song Z, Yan S (2015) STAP: spatial-temporal attention-aware pooling for action recognition. IEEE Trans Circuits Syst Video Technol 25(1):77\u201386","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"3685_CR83","doi-asserted-by":"crossref","unstructured":"Tian Y, Ruan Q, An G, Fu Y (2016) Action recognition using local consistent group sparse coding with spatio-temporal structure. In: Proceedings of the 2016 ACM on multimedia conference, pp 317\u2013321","DOI":"10.1145\/2964284.2967234"},{"issue":"7","key":"3685_CR84","doi-asserted-by":"crossref","first-page":"1494","DOI":"10.1109\/TMM.2017.2674622","volume":"19","author":"W Xu","year":"2017","unstructured":"Xu W, Miao Z, Zhang XP, Tian Y (2017) A hierarchical spatio-temporal model for human activity recognition. IEEE Trans Multimedia 19(7):1494\u20131509","journal-title":"IEEE Trans Multimedia"},{"key":"3685_CR85","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.eswa.2017.01.008","volume":"75","author":"Y Yi","year":"2017","unstructured":"Yi Y, Zheng Z, Lin M (2017) Realistic action recognition with salient foreground trajectories. Expert Syst Appl 75:44\u201355","journal-title":"Expert Syst Appl"},{"key":"3685_CR86","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.eswa.2017.02.020","volume":"78","author":"Y Yi","year":"2017","unstructured":"Yi Y, Cheng Y, Xu C (2017) Mining human movement evolution for complex action recognition. Expert Syst Appl 78:259\u2013272","journal-title":"Expert Syst Appl"},{"issue":"3","key":"3685_CR87","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1109\/TPAMI.2017.2691768","volume":"40","author":"H Rahmani","year":"2018","unstructured":"Rahmani H, Mian A, Shah M (2018) Learning a deep model for human action recognition from novel viewpoints. IEEE Trans Pattern Anal Mach Intell 40(3):667\u2013681","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"12","key":"3685_CR88","doi-asserted-by":"crossref","first-page":"3819","DOI":"10.1016\/j.patcog.2014.07.006","volume":"47","author":"L Liu","year":"2014","unstructured":"Liu L, Shao L, Zheng F, Li X (2014) Realistic action recognition via sparsely-constructed Gaussian processes. Pattern Recognit 47(12):3819\u20133827","journal-title":"Pattern Recognit"},{"issue":"4","key":"3685_CR89","doi-asserted-by":"crossref","first-page":"1174","DOI":"10.1016\/j.patcog.2014.11.013","volume":"48","author":"O Kihl","year":"2015","unstructured":"Kihl O, Picard D, Gosselin PH (2015) A unified framework for local visual descriptors evaluation. Pattern Recognit 48(4):1174\u20131184","journal-title":"Pattern Recognit"},{"key":"3685_CR90","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1016\/j.neucom.2015.09.074","volume":"174","author":"Q Sun","year":"2016","unstructured":"Sun Q, Liu H, Ma L, Zhang T (2016) A novel hierarchical Bag-of-Words model for compact action representation. Neurocomputing 174:722\u2013732","journal-title":"Neurocomputing"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-018-3685-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-018-3685-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-018-3685-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,4,19]],"date-time":"2020-04-19T18:03:47Z","timestamp":1587319427000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-018-3685-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8,21]]},"references-count":90,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2020,5]]}},"alternative-id":["3685"],"URL":"https:\/\/doi.org\/10.1007\/s00521-018-3685-9","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2018,8,21]]},"assertion":[{"value":"25 May 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 August 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 August 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"All the authors of the manuscript declared that there are no potential conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"All the authors of the manuscript declared that there is no research involving human participants and\/or animal.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and animal rights"}},{"value":"All the authors of the manuscript declared that there is no material that required informed consent.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}