Spectral learning of latent semantics for action recognition

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2011, Pages 1503-1510

  Lu, Zhiwu ^a   Peng, Yuxin ^a   Ip, Horace H S ^b  

^a PEKING UNIVERSITY   (China)

^b CITY UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

ACTION RECOGNITION; DATA SETS; GRAPH CONSTRUCTION; HIGH-LEVEL FEATURES; HISTOGRAM INTERSECTION KERNELS; HYPER GRAPH; LARGE VOCABULARY; LATENT SEMANTIC ANALYSIS; LATENT SEMANTICS; LINEAR RECONSTRUCTION; MANIFOLD LEARNING; MID-LEVEL FEATURES; SEMANTIC GAP; SPARSE CODING; SPECTRAL CLUSTERING; SPECTRAL LEARNING; SPECTRAL METHODS; STANDARD ACTION; TOPIC MODEL;

IMAGE PROCESSING; SPECTROSCOPY;

GRAPH THEORY;

EID: 84856661234     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2011.6126408     Document Type: Conference Paper

References (28)

1. S. Agarwal, K. Branson, and S. Belongie. Higher order learning with graphs. In ICML, pages 17-24, 2006. 2, 4
2. M. Balasubramanian and E. Schwartz. The isomap algorithm and topological stability. Science, 295(5552):7-7, 2002. 5, 6
3. L. Ballan, M. Bertini, A. D. Bimbo, L. Seidenari, and G. Serra. Effective codebooks for human action categorization. In ICCV International Workshop on Video-Oriented Object and Event Classification, pages 506-513, 2009. 1
4. M. Belkin and P. Niyogi. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation, 15(6):1373-1396, 2003. 5, 6
5. D. Blei, A. Ng, and M. Jordan. Latent Dirichlet allocation. JMLR, 3:993-1022, 2003. 1
6. M. Bregonzio, S. Gong, and T. Xiang. Recognising action as clouds of space-time interest points. In CVPR, pages 1948-1955, 2009. 1
7. L. Cao, Y. Tian, Z. Liu, B. Yao, Z. Zhang, and T. Huang. Action detection using multiple spatial-temporal interest point features. In ICME, pages 340-345, 2010. 2, 7
8. P. Dollar, V. Rabaud, G. Cottrell, and S. Belongie. Behavior recognition via sparse spatio-temporal features. In IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, pages 65-72, 2005. 1, 5, 7
9. D. Donoho. For most large underdetermined systems of linear equations the minimal -1-norm solution is also the sparsest solution. Communications on Pure and Applied Mathematics, 59(7):797-829, 2004. 2, 3
10. T. Hofmann. Unsupervised learning by probabilistic latent semantic analysis. Machine Learning, 41:177-196, 2001. 1
11. A. Kovashka and K. Grauman. Learning a hierarchy of discriminative space-time neighborhood features for human action recognition. In CVPR, pages 2046-2053, 2010. 1, 7
12. S. Lafon and A. Lee. Diffusion maps and coarse-graining: A unified framework for dimensionality reduction, graph partitioning, and data set parameterization. TPAMI, 28(9):1393-1403, 2006. 2
13. I. Laptev, M. Marszalek, C. Schmid, and B. Rozenfeld. Learning realistic human actions from movies. In CVPR, 2008. 1, 2, 7
14. J. Liu, J. Luo, and M. Shah. Recognizing realistic actions from videos in the wild. In CVPR, pages 1996-2003, 2009. 1, 2, 5, 6, 7
15. J. Liu and M. Shah. Learning human actions via information maximization. In CVPR, 2008. 1, 2, 5, 6, 7
16. J. Liu, Y. Yang, and M. Shah. Learning semantic visual vocabularies using diffusion distance. In CVPR, pages 461-468, 2009. 2, 3, 5, 6, 7
17. W.-L. Lu, K. Okuma, and J. Little. Tracking and recognizing actions of multiple hockey players using the boosted particle filter. Image and Vision Computing, 27(1-2):189-205, 2009. 1
18. A. Ng, M. Jordan, and Y. Weiss. On spectral clustering: Analysis and an algorithm. In Advances in Neural Information Processing Systems 14, pages 849-856, 2002. 2, 4
19. J. Niebles, H. Wang, and L. Fei-Fei. Unsupervised learning of human action categories using spatial-temporal words. IJCV, 79(3):299-318, 2008. 1, 2, 3, 7
20. G. Papadopoulos, A. Briassouli, V. Mezaris, I. Kompatsiaris, and M. Strintzis. Statistical motion information extraction and representation for semantic video analysis. TCSVT, 19(10):1513-1528, 2009. 2, 7
21. V. Parameswaran and R. Chellappa. View invariance for human action recognition. IJCV, 66(1):83-101, 2006. 1
22. C. Schuldt, I. Laptev, and B. Caputo. Recognizing human actions: A local SVM approach. In ICPR, volume 3, pages 32-36, 2004. 1, 2, 5, 7
23. L. Sun, S. Ji, and J. Ye. Hypergraph spectral learning for multi-label classification. In KDD, pages 668-676, 2008. 2, 4
24. P. Turaga, R. Chellappa, V. Subrahmanian, and O. Udrea. Machine recognition of human activities: A survey. TCSVT, 18(11):1473-1488, 2008. 1
25. Y. Wang and G. Mori. Human action recognition by semilatent topic models. TPAMI, 31(10):1762-1774, 2009. 1, 2, 3
26. J. Wright, A. Yang, A. Ganesh, S. Sastry, and Y. Ma. Robust face recognition via sparse representation. TPAMI, 31(2):210-227, 2009. 2, 3
27. S. Yan, D. Xu, B. Zhang, H. Zhang, Q. Yang, and S. Lin. Graph embedding and extensions: A general framework for dimensionality reduction. TPAMI, 29(1):40-51, 2007. 2
28. D. Zhou, J. Huang, and B. Schölkopf. Learning with hypergraphs: Clustering, classification, and embedding. In Advances in Neural Information Processing Systems 19, pages 1601-1608, 2007. 2, 4