A set of co-occurrence matrices on the intrinsic manifold of human silhouettes for action recognition

CIVR 2010 - 2010 ACM International Conference on Image and Video Retrieval

Volumn , Issue , 2010, Pages 454-461

  Zheng, Feng ^a,b   Shao, Ling ^c   Song, Zhan ^a,b  

^a SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY   (China)

^b CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

^c UNIVERSITY OF SHEFFIELD   (United Kingdom)

Author keywords

Action recognition; Co occurrence matrices; Graph embedding

Indexed keywords

ACTION RECOGNITION; CO-OCCURRENCE-MATRIX; DESCRIPTORS; GRAPH EMBEDDINGS; HUMAN SILHOUETTE; INTRINSIC SHAPE; LABEL INFORMATION; MONOCULAR VIDEO; NONLINEAR ALGORITHMS; SPATIAL INFORMATIONS; TEMPORAL INFORMATION;

COMPUTER VISION;

EID: 77955871775     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1816041.1816108     Document Type: Conference Paper

References (23)

1. M. Blank, L. Gorelick, E. Shechtman, M. Irani, and R. Basri. Actions as space-time shapes. Tenth IEEE International Conference on Computer Vision, volume 2, pages 1395-1402 Vol. 2, oct. 2005.
2. A. F. Bobick, J. W. Davis, I. C. Society, and I. C. Society. The recognition of human movement using temporal templates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23:257-267, 2001.
3. T.-J. Chin, L. Wang, K. Schindler, and D. Suter. Extrapolating learned manifolds for human activity recognition. IEEE International Conference on Image Processing, volume 1, pages I-381-I-384, 16 2007-oct. 19 2007.
4. A. Elgammal and C. su Lee. Inferring 3d body pose from silhouettes using activity manifold learning. In CVPR, pages 681-688, 2004.
5. R. M. Haralick, Dinstein, and K. Shanmugam. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3:610-621, November 1973.
6. I. T. Jolliffe. Principal component analysis. New York: Springer-Verlag, 2(2):1-8, June 2003.
7. J. B. Tenenbaum, V. de Silva, J. C. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 2(2):2319-2323, June 2000.
8. K. Jia and D.-Y. Yeung. Human action recognition using local spatio-temporal discriminant embedding. IEEE Conference on Computer Vision and Pattern Recognition, pages 1-8, june 2008.
9. V. Kellokumpu, G. Zhao, and M. Pietikäinen. Recognition of human actions using texture descriptors. Machine Vision and Applications, 2009.
10. L. Wang and D. Suter. Recognizing human activities from silhouettes: Motion subspace and factorial discriminative graphical model. IEEE CVPR, 2(2):1-8, June 2007.
11. F. Liu and Y. Jia. Human action recognition using manifold learning and hidden conditional random fields. The 9th International Conference for Young Computer Scientists, pages 693-698, nov. 2008.
12. R. D. Cook. Fisher lecture: Dimension reduction in regression. Statist. Sci., 2(2):1-26, June 2007.
13. R.R. Coifman, S. Lafon, A.B. Lee, M. Maggioni, B. Nadler, F. Warner, and S. Zucker. Geomatic diffusions as a tool for harmonics amalysis and structure definition of data. Proc. Nat'l academy of sciences, 102(21):7426-7431, June 2005.
14. S. T. Roweis and L. K. Saul. Nonlinear dimensionality reduction by locally linear embedding. Science, 2(2):2323-2326, June 2000.
15. S. Tabbone, L. Wendling, and J.-P. Salmon. A new shape descriptor defined on the radon transform. Computer Vision and Image Understanding archive, 102(21):42-51, June 2006.
16. C. Sminchisescu, A. Kanujia, Z. Li, and D. Metaxas. Conditional visual tracking in kernel space. In Proc. NIPS, page 11, 2005.
17. R. Souvenir and K. Parrigan. Viewpoint manifolds for action recognition. Image Video Process., 2009:1-1, 2009.
18. D. Weinland, R. Ronfard, and E. Boyer. Free viewpoint action recognition using motion history volumes. Comput. Vis. Image Underst., 104(2):249-257, 2006.
19. X. He and P. Niyogi. Locality preserving projections. In Advances in Neural Information Processing Systems, 16(21):37, June 2004.
20. S. Yan, D. Xu, B. Zhang, H.-J. Zhang, Q. Yang, and S. Lin. Graph embedding and extensions: A general framework for dimensionality reduction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(1):40-51, jan. 2007.
21. A. Yilmaz and M. Shah. Actions sketch: a novel action representation. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume 1, pages 984 - 989 vol. 1, June 2005.
22. F. Zheng and Z. Song. Diffusion maps for dimensionality reduction with partially labeled samples. The 2nd International Conference on Computer and Automation Engineering, 2:68-73, Feb. 2010.
23. F. Zheng, L. Shao and Z. Song, Eigen-space Learning Using Semi-supervised Diffusion Maps for Human Action Recognition. Appear in the ACM International Conference on Image and Video Retrieval (CIVR 2010). July, Xi'an, China.