Energy-based model of least squares twin Support Vector Machines for human action recognition

Signal Processing

Volumn 104, Issue , 2014, Pages 248-257

  Nasiri, Jalal A ^a   Moghadam Charkari, Nasrollah ^a   Mozafari, Kourosh ^a  

^a TARBIAT MODARES UNIVERSITY   (Iran)

Author keywords

Human action recognition; Least square method; Multi class classification; Spatio temporal feature; Twin Support Vector Machine

Indexed keywords

EID: 84900390433     PISSN: 01651684     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.sigpro.2014.04.010     Document Type: Article

References (41)

1. L. Chen, H. Wei, J. Ferryman, A survey of human motion analysis using depth imagery, Pattern Recognit. Lett.
2. L. Liu, L. Shao, and P. Rockett Human action recognition based on boosted feature selection and naive Bayes nearest-neighbor classification Signal Process. 93 6 2013 1521 1530 10.1016/j.sigpro.2012.07.017
3. Y. Yi, and Y. Lin Human action recognition with salient trajectories Signal Process. 93 11 2013 2932 2941
4. G. Goudelis, K. Karpouzis, and S. Kollias Exploring trace transform for robust human action recognition Pattern Recognit. 46 12 2013 3238 3248
5. C. Cortes, and V. Vapnik Support-vector networks Mach. Learn. 20 3 1995 273 297
6. L. Zhang, M. Song, X. Liu, J. Bu, and C. Chen Fast multi-view segment graph kernel for object classification Signal Process. 93 6 2013 1597 1607
7. S. Mehta, and N. Lingayat Development of entropy based algorithm for cardiac beat detection in 12-lead electrocardiogram Signal Process. 87 12 2007 3190 3201 (Pubitemid 47259199)
8. X. Song, L. Jiao, S. Yang, X. Zhang, and F. Shang Sparse coding and classifier ensemble based multi-instance learning for image categorization Signal Process. 93 1 2013 1 11
9. R. Singh, M. Vatsa, and A. Noore Improving verification accuracy by synthesis of locally enhanced biometric images and deformable model Signal Process. 87 11 2007 2746 2764 (Pubitemid 47061433)
10. R. Khemchandani, and S. Chandra et al. Twin support vector machines for pattern classification IEEE Trans. Pattern Anal. Mach. Intell. 29 5 2007 905 910 (Pubitemid 46534787)
11. M.A. Kumar, and M. Gopal Least squares twin support vector machines for pattern classification Expert Syst. Appl. 36 4 2009 7535 7543
12. J. Aggarwal, and M.S. Ryoo Human activity analysis a review ACM Comput. Surv. (CSUR) 43 3 2011 16
13. D. Weinland, R. Ronfard, and E. Boyer A survey of vision-based methods for action representation, segmentation and recognition Comput. Vis. Image Underst. 115 2 2011 224 241
14. R. Poppe A survey on vision-based human action recognition Image Vis. Comput. 28 6 2010 976 990
15. Y. Wang, K. Huang, T. Tan, Human activity recognition based on r transform, in: IEEE Conference on Computer Vision and Pattern Recognition, 2007, CVPR07, IEEE, 2007, pp. 1-8.
16. V. Kellokumpu, G. Zhao, M. Pietikäinen, Human activity recognition using a dynamic texture based method, in: BMVC, 2008, pp. 1-10.
17. A. Ghodrati, and S. Kasaei Human action categorization using discriminative local spatio-temporal feature weighting Intell. Data Anal. 16 4 2012 537 550
18. I. Laptev, M. Marszalek, C. Schmid, B. Rozenfeld, Learning realistic human actions from movies, in: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, IEEE, 2008, pp. 1-8.
19. L. Shao, R. Gao, Y. Liu, and H. Zhang Transform based spatio-temporal descriptors for human action recognition Neurocomputing 74 6 2011 962 973
20. I. Laptev On space-time interest points Int. J. Comput. Vis. 64 2-3 2005 107 123 (Pubitemid 41314645)
21. P. Dollár, V. Rabaud, G. Cottrell, S. Belongie, Behavior recognition via sparse spatio-temporal features, in: 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 2005, IEEE, 2005, pp. 65-72. (Pubitemid 46178025)
22. P. Scovanner, S. Ali, M. Shah, A 3-dimensional sift descriptor and its application to action recognition, in: Proceedings of the 15th International Conference on Multimedia, ACM, 2007, pp. 357-360.
23. H. Wang, A. Klaser, C. Schmid, C.-L. Liu, Action recognition by dense trajectories, in: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2011, pp. 3169-3176.
24. X. Yan, Y. Luo, Making full use of spatial-temporal interest points: an adaboost approach for action recognition, in: 2010 17th IEEE International Conference on Image Processing (ICIP), IEEE, 2010, pp. 4677-4680.
25. Z. Lu, and Y. Peng Latent semantic learning with structured sparse representation for human action recognition Pattern Recognit. 46 7 2013 1799 1809
26. J.A. Suykens, J. De Brabanter, L. Lukas, and J. Vandewalle Weighted least squares support vector machines robustness and sparse approximation Neurocomputing 48 1 2002 85 105 (Pubitemid 36221490)
27. B.D. Ripley Pattern Recognition and Neural Networks 2007 Cambridge University Press
28. R.N. Ganesh, and K.K. Dinesh Twin SVM for gesture classification using the surface electromyogram IEEE Trans. Inf. Technol. Biomed. 14 2 2010 301 308
29. Z. Gao, M.-Y. Chen, A. Hauptmann, A. Cai, Comparing evaluation protocols on the kth dataset, in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) LNCS, vol. 6219, 2010, pp. 88-100.
30. A. Fathi, G. Mori, Action recognition by learning mid-level motion features, in: IEEE Conference on Computer Vision and Pattern Recognition, 2008, CVPR 2008, IEEE, 2008, pp. 1-8.
31. Z. Jiang, Z. Lin, and L.S. Davis Recognizing human actions by learning and matching shape-motion prototype trees IEEE Trans. Pattern Anal. Mach. Intell. 34 3 2012 533 547
32. J.C. Niebles, H. Wang, and L. Fei-Fei Unsupervised learning of human action categories using spatial-temporal words Int. J. Comput. Vis. 79 3 2008 299 318
33. J. Liu, S. Ali, M. Shah, Recognizing human actions using multiple features, in: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, IEEE, 2008, pp. 1-8.
34. M. Bregonzio, S. Gong, T. Xiang, Recognising action as clouds of space-time interest points, in: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, IEEE, 2009, pp. 1948-1955.
35. H. Wang, M. M. Ullah, A. Klaser, I. Laptev, C. Schmid, et al., Evaluation of local spatio-temporal features for action recognition, in: BMVC 2009-British Machine Vision Conference, 2009.
36. C. Schuldt, I. Laptev, B. Caputo, Recognizing human actions: a local SVM approach, in: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 3, IEEE, 2004, pp. 32-36.
37. S.-F. Wong, T.-K. Kim, R. Cipolla, Learning motion categories using both semantic and structural information, in: IEEE Conference on Computer Vision and Pattern Recognition, 2007, CVPR'07, IEEE, 2007, pp. 1-6.
38. H. Jhuang, T. Serre, L. Wolf, T. Poggio, A biologically inspired system for action recognition, in: IEEE 11th International Conference on Computer Vision, ICCV 2007, IEEE, 2007, pp. 1-8.
39. A. Klaser, M. Marszalek, A Spatio-Temporal Descriptor Based on 3D-Gradients, 2008.
40. J. Liu, M. Shah, Learning human actions via information maximization, in: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, IEEE, 2008, pp. 1-8.
41. A. Kovashka, K. Grauman, Learning a hierarchy of discriminative space-time neighborhood features for human action recognition, in: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2010, pp. 2046-2053.