Single/multi-view human action recognition via regularized multi-task learning

Neurocomputing

Volumn 151, Issue P2, 2015, Pages 544-553

  Liu, An An ^a   Xu, Ning ^a   Su, Yu Ting ^a   Lin, Hong ^a   Hao, Tong ^b   Yang, Zhao Xuan ^a  

^a TIANJIN UNIVERSITY   (China)

^b TIANJIN NORMAL UNIVERSITY   (China)

Author keywords

Graph structure; Human action recognition; Multi task learning; Pyramid partwise bag of words; Sparsity

Indexed keywords

GRAPH STRUCTURES; GRAPHIC METHODS; LEARNING SYSTEMS; SUPPORT VECTOR MACHINES;

BAG OF WORDS; HUMAN BODIES; HUMAN-ACTION RECOGNITION; IMPROVE PERFORMANCE; LOW DIMENSIONAL; MULTIPLE VIEWS; SPARSITY; STATE-OF-THE-ART METHODS;

MULTI-TASK LEARNING;

ARTICLE; CONTROLLED STUDY; CORRELATION ANALYSIS; GRAPH; HUMAN; HUMAN ACTION RECOGNITION; INTERMETHOD COMPARISON; MACHINE LEARNING; MATHEMATICAL PARAMETERS; PERFORMANCE; PYRAMID PARTWISE BAG OF WORDS; REGULARIZED MULTI TASK LEARNING; SPARSITY; VISION;

EID: 84919627765     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2014.04.090     Document Type: Article

References (74)

1. Aggarwal J.K., Cai Q. Human motion analysis. a review. Comput. Vis. Image Underst. 1999, 428-440.
2. Aggarwal J.K., Ryoo M.S. Human activity analysis. a review. ACM Comput. Surv. 2011, 43(3):1-43.
3. Liu A., Gao Z., Hao T., Su Y., Yang Z. Partwise bag of words-based multi-task learning for human action recognition. Electron. Lett. 2013, 803-804.
4. Tian Y., Cao L., Liu Z., Zhang Z. Hierarchical filtered motion for action recognition in crowded videos. IEEE Trans. Syst. Man Cybern. Part C 2012, 313-323.
5. Shen J., Tao D., Li X. Modality mixture projections for semantic video event detection. IEEE Trans. Circuits Syst. Video Technol. 2008, 1587-1596.
6. A. Liu, W. Nie, Y. Su, et al., Coupled hidden conditional random fields for rgb-d human action recognition, Signal Process.
7. Gao Y., Wang M., Tao D., Ji R., Dai Q. 3-d object retrieval and recognition with hypergraph analysis. IEEE Trans. Image Process. 2012, 4290-4303.
8. A. Liu, Y. Su, P. Jia, et al., Multipe/single-view human action recognition via part-induced multitask structural learning, IEEE Trans. Cybern.
9. Q.V. Le, W.Y. Zou, S.Y. Yeung, A.Y. Ng, Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis, in: CVPR'11, 2011, pp. 3361-3368.
10. Ji R., Duan L.-Y., Chen J., Yao H., Yuan J., Rui Y., Gao W. Location discriminative vocabulary coding for mobile landmark search. Int. J. Comput. Vis. 2012, 290-314.
11. Shyu M.-L., Xie Z., Chen M., Chen S.-C. Video semantic event/concept detection using a subspace-based multimedia data mining framework. IEEE Trans. Multimed. 2008, 252-259.
12. L. Zhang, Y. Gao, R. Zimmermann, Q. Tian, X. Li, Fuzing multi-channel local and global structural cues for photo aesthetics evaluation, IEEE Trans. Image Process. (2014) in press.
13. I. Laptev, T. Lindeberg, Space-time interest points, in: ICCV'03, 2003, pp. 432-439.
14. P. Dollar, V. Rabaud, G. Cottrell, S. Belongie, Behavior recognition via sparse spatio-temporal features, in: VS-PETS, 2005.
15. G. Willems, T. Tuytelaars, L.J.V. Gool, An efficient dense and scale-invariant spatio-temporal interest point detector, in: ECCV'08, 2008, pp. 650-663.
16. I. Laptev, Local spatio-temporal image features for motion interpretation (Ph.D. thesis), department of Numerical Analysis and Computer Science (NADA),KTH, 2004.
17. I. Laptev, T. Lindeberg, Local descriptors for spatio-temporal recognition, in: First International Workshop on Spatial Coherence for Visual Motion Analysis, 2004, pp. 91-103.
18. P. Scovanner, S. Ali, M. Shah, A 3-dimensional sift descriptor and its application to action recognition, in: ACM Multimedia'07, 2007, pp. 357-360.
19. H. Jhuang, T. Serre, L. Wolf, T. Poggio, A biologically inspired system for action recognition, in: ICCV'07, 2007, pp. 1-8.
20. I. Laptev, M. Marszalek, C. Schmid, B. Rozenfeld, Learning realistic human actions from movies, in: CVPR'08, 2008.
21. A. Klaser, M. Marszalek, C. Schmid, A spatio-temporal descriptor based on 3d-gradients, in: BMVC'08, 2008.
22. F.-F. Li, P. Perona, A Bayesian hierarchical model for learning natural scene categories, in: CVPR'05, 2005, pp. 524-531.
23. Ji R., Yao H., Liu W., Sun X., Tian Q. Task-dependent visual-codebook compression. IEEE Trans. Image Process. 2012, 2282-2293.
24. Z. Gao, A. Liu, H. Zhang, G. Xu, Y. Xue, Human action recognition based on sparse representation induced by l1/l2 regulations, in: ICPR'12, 2012, pp. 1868-1871.
25. H. Wang, M. M. Ullah, A. Klaser, I. Laptev, C. Schmid, Evaluation of local spatio-temporal features for action recognition, in: BMVC'09, 2009.
26. Gorelick L., Blank M., Shechtman E., Irani M., Basri R. Actions as space-time shapes. IEEE Trans. Pattern Anal. Mach. Intell. 2007, 23:2247-2253.
27. M. Blank, L. Gorelick, E. Shechtman, M. Irani, R. Basri, Actions as space-time shapes, in: ICCV'05, 2005, pp. 1395-1402.
28. C. Schuldt, I. Laptev, B. Caputo, Recognizing human actions: a local svm approach, in: ICPR'04, 2004, pp. 32-36.
29. M.D. Rodriguez, J. Ahmed, M. Shah, Action mach a spatio-temporal maximum average correlation height filter for action recognition, in: CVPR'08, 2008, pp. 1-1.
30. M. Marszalek, I. Laptev, C. Schmid, Actions in context, in: CVPR'09, 2009, pp. 2929-2936.
31. J. Liu, J. Luo, M. Shah, Recognizing realistic actions from videos in the wild, in: CVPR'09, 2009, pp. 1996-2003.
32. Y. Yang, Q. Liu, R. Ji, Y. Gao, Remote dynamic three-dimensional scene reconstruction, PLoS One (2013).
33. R. Ji, L.-Y. Duan, J. Chen, T. Huang, W. Gao, Mining compact 3d patterns for low bit rate mobile visual search, IEEE Trans. Image Process. (2014) in press.
34. Gao Y., Dai Q., Zhang N. 3d model comparison using spatial structure circular descriptor. Pattern Recognit. 2010, 1142-1151.
35. Liu W., Tao D. Multiview Hessian regularization for image annotation. IEEE Trans. Image Process. 2013, 2676-2687.
36. Wang M., Gao Y., Lu K., Rui Y. View-based discriminative probabilistic modeling for 3d object retrieval and recognition. IEEE Trans. Image Process. 2013, 1395-1407.
37. A. Yilmaz, M. Shah, Actions sketch: a novel action representation, in: CVPR (1)'05, 2005, pp. 984-989.
38. Rao C., Yilmaz A., Shah M. View-invariant representation and recognition of actions. Int. J. Comput. Vis. 2002, 203-226.
39. Gao Y., Wang M., Ji R., Wu X., Dai Q. 3-d object retrieval with Hausdorff distance learning. IEEE Trans. Ind. Electron. 2014, 2088-2098.
40. Gao Y., Wang M., Zha Z.-J., Tian Q., Dai Q., Zhang N. Less is more. efficient 3-d object retrieval with query view selection. IEEE Trans. Multimed. 2011, 1007-1018.
41. F. Lv, R. Nevatia, Single view human action recognition using key pose matching and viterbi path searching, in: CVPR'07, 2007.
42. R. Li, T. Tian, S. Sclaroff, Simultaneous learning of nonlinear manifold and dynamical models for high-dimensional time series, in: ICCV'07, 2007, pp. 1-8.
43. Gao Y., Tang J., Hong R., Yan S., Dai Q., Zhang N., Chua T.-S. Camera constraint-free view-based 3-d object retrieval. IEEE Trans. Image Process. 2012, 2269-2281.
44. A. Farhadi, M. Tabrizi, Learning to recognize activities from the wrong view point, in: ECCV'08, 2008, pp. 154-166.
45. J. Liu, M. Shah, B. Kuipers, S. Savarese, Cross-view action recognition via view knowledge transfer, in: CVPR'11, 2011, pp. 3209-3216.
46. Y. Wen, Y. Gao, R. Hong, H.-B. Luan, Q. Liu, J. Shen, R. Ji, View-based 3d object retrieval by bipartite graph matching, in: ACM Multimedia'12, 2012, pp. 897-900.
47. Wang M., Hua X.-S., Hong R., Tang J., Qi G.-J., Song Y. Unified video annotation via multi-graph learning. IEEE Trans. Circuits Syst. Video Technol. 2009, 1142-1151.
48. Zhang L., Song M., Zhao Q., Liu X., Bu J., Chen C. Probabilistic graphlet transfer for photo cropping. IEEE Trans. Image Process. 2013, 802-815.
49. Zhang Y., Yan C., Dai F., Ma Y. Efficient parallel framework for h.264/avc deblocking filter on many-core platform. IEEE Trans. Multimed. 2012, 510-524.
50. Lee1 D.D., Seung H.S. Learning the parts of objects by non-negative matrix factorization. Nature 1999, 788-791.
51. A. Kovashka, K. Grauman, Learning a hierarchy of discriminative space-time neighborhood features for human action recognition, in: CVPR'10, 2010, pp. 2046-2053.
52. Bao B.-K., Zhu G., Shen J., Yan S. Robust image analysis with sparse representation on quantized visual features. IEEE Trans. Image Process. 2013, 860-871.
53. M. Zhou, H. Chen, J.W. Paisley, L. Ren, G. Sapiro, L. Carin, Non-parametric Bayesian dictionary learning for sparse image representations, in: NIPS'09, 2009, pp. 2295-2303.
54. Z. Yuan, E. Oja, Projective nonnegative matrix factorization for image compression and feature extraction, in: Lecture Notes in Computer Science, 2005, pp. 333-342.
55. Luo Y., Tao D., Geng B., Xu C., Maybank S.J. Manifold regularized multitask learning for semi-supervised multilabel image classification. IEEE Trans. Image Process. 2013, 523-536.
56. Liu Q., Yang Y., Ji R., Gao Y., Yu L. Cross-view down/up-sampling method for multiview depth video coding. IEEE Signal Process. Lett. 2012, 295-298.
57. S. Lazebnik, C. Schmid, J. Ponce, Beyond bags of features: spatial pyramid matching for recognizing natural scene categories, in: CVPR (2)'06, 2006, pp. 2169-2178.
58. J. Wang, Z. Liu, Y. Wu, J. Yuan, Mining actionlet ensemble for action recognition with depth cameras, in: CVPR'12, 2012, pp. 1290-1297.
59. L. Xia, J. Aggarwal, Spatio-temporal depth cuboid similarity feature for activity recognition using depth camera, in: CVPR'13, 2013.
60. Felzenszwalb P.F., Girshick R.B., McAllester D.A., Ramanan D. Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 2010, 1627-1645.
61. B. Ni, G. Wang, P. Moulin, Rgbd-hudaact: a color-depth video database for human daily activity recognition, in: Computational Methods for the Innovative Design of Electrical Devices'11, 2011, pp. 1147-1153.
62. Nesterov Y. Introductory Lectures on Convex Optimization: A Basic Course 2004, Springer.
63. P. Tseng, On accelerated proximal gradient methods for convex-concave optimization, SIAM J. Optim. (2014) submitted for publication.
64. Tian Y., Cao L., Liu Z., Zhang Z. Hierarchical filtered motion for action recognition in crowded videos. IEEE Trans. Syst. Man Cybern. Part C 2012, 313-323.
65. J. Wang, Z. Liu, Y. Wu, J. Yuan, Mining actionlet ensemble for action recognition with depth cameras, in: CVPR'12, 2012, pp. 1290-1297.
66. Y.-C. Lin, M.-C. Hu, W.-H. Cheng, Y.-H. Hsieh, H.-M. Chen, Human action recognition and retrieval using sole depth information, in: ACM Multimedia'12, 2012, pp. 1053-1056.
67. Y. Ke, R. Sukthankar, M. Hebert, Efficient visual event detection using volumetric features, in: ICCV'05, 2005, pp. 166-173.
68. S. Nowozin, G.H. Bakir, K. Tsuda, Discriminative subsequence mining for action classification, in: ICCV'07, 2007, pp. 1-8.
69. A. Fathi, G. Mori, Action recognition by learning mid-level motion features, in: CVPR'08, 2008.
70. Niebles J.C., Wang H., Li F.-F. Unsupervised learning of human action categories using spatial-temporal words. Int. J. Comput. Vis. 2008, 299-318.
71. Ballan L., Bertini M., Bimbo A., Seidenari L., Serra G. Effective codebooks for human action representation and classification in unconstrained videos. IEEE Trans. Multimed. 2012, 1234-1245.
72. Zhang Z., Tao D. Slow feature analysis for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 2012, 436-450.
73. Yang Y., Saleemi I., Shah M. Discovering motion primitives for unsupervised grouping and one-shot learning of human actions, gestures, and expressions. IEEE Trans. Pattern Anal. Mach. Intell. 2013, 23:1635-1648.
74. Zhen X., Shao L., Tao D., Li X. Embedding motion and structure features for action recognition. IEEE Trans. Circuits Syst. Video Technol. 2013, 1182-1190.