Unsupervised temporal commonality discovery

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7575 LNCS, Issue PART 4, 2012, Pages 373-387

  Chu, Wen Sheng ^a   Zhou, Feng ^a   De La Torre, Fernando ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

branch and bound; Temporal bag of words; temporal commonality discovery

Indexed keywords

BAG OF WORDS; BRANCH AND BOUNDS; EXHAUSTIVE SEARCH; FACIAL ACTION; GLOBAL OPTIMAL SOLUTIONS; HUMAN ACTIONS; MOTION CAPTURE DATA; NATURAL EXTENSION; TEMPORAL COMMONALITY DISCOVERY; TEMPORAL PATTERN; TIGHT BOUND; VISUAL CONTENT; VISUAL DATA;

ALGORITHMS; COMPUTER VISION; LINEAR PROGRAMMING; SEMANTICS;

PROBLEM SOLVING;

EID: 84867844545     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33765-9_27     Document Type: Conference Paper

References (34)

1. http://mocap.cs.cmu.edu/
2. An, S., Peursum, P., Liu, W., Venkatesh, S.: Efficient subwindow search with submodular score functions. In: CVPR (2011)
3. Balakrishnan, V., Boyd, S., Balemi, S.: Branch and bound algorithm for computing the minimum stability degree of parameter-dependent linear systems. International Journal of Robust and Nonlinear Control 1(4), 295-317 (1991)
4. Barbič, J., Safonova, A., Pan, J.Y., Faloutsos, C., Hodgins, J.K., Pollard, N.S.: Segmenting motion capture data into distinct behaviors. In: Proc. of Graphics Interface (2004)
5. Bartlett, M.S., Littlewort, G.C., Frank, M.G., Lainscsek, C., Fasel, I.R., Movellan, J.R.: Automatic recognition of facial actions in spontaneous expressions. Journal of Multimedia 1(6), 22-35 (2006)
6. Boiman, O., Irani, M.: Detecting irregularities in images and in video. In: ICCV (2005)
7. Brendel, W., Todorovic, S.: Learning spatiotemporal graphs of human activities. In: ICCV (2011)
8. Chu, W.-S., Chen, C.-P., Chen, C.-S.: MOMI-Cosegmentation: Simultaneous Segmentation of Multiple Objects among Multiple Images. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part I. LNCS, vol. 6492, pp. 355-368. Springer, Heidelberg (2011)
9. Everingham, M., Zisserman, A., Williams, C.I., Van Gool, L.: The PASCAL visual object classes challenge 2006 results. In: 2th PASCAL Challenge (2006)
10. Gusfield, D.: Algorithms on strings, trees, and sequences: computer science and computational biology. Cambridge Univ. Press (1997)
11. Han, D., Bo, L., Sminchisescu, C.: Selection and Context for Action Recognition. In: ICCV (2009)
12. Hoai, M., Zhong Lan, Z., De la Torre, F.: Joint segmentation and classification of human actions in video. In: CVPR (2011)
13. Lampert, C., Blaschko, M., Hofmann, T.: Efficient subwindow search: A branch and bound framework for object localization. PAMI 31(12), 2129-2142 (2009)
14. Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: CVPR (2008)
15. Liu, H., Yan, S.: Common visual pattern discovery via spatially coherent correspondences. In: CVPR (2010)
16. Liu, J., Shah, M., Kuipers, B., Savarese, S.: Cross-view action recognition via view knowledge transfer. In: CVPR (2011)
17. Maier, D.: The complexity of some problems on subsequences and supersequences. Journal of the ACM 25(2), 322-336 (1978)
18. Minnen, D., Isbell, C., Essa, I., Starner, T.: Discovering multivariate motifs using subsequence density estimation. In: AAAI (2007)
19. Mueen, A., Keogh, E.: Online discovery and maintenance of time series motifs. In: KDD (2010)
20. Mukherjee, L., Singh, V., Peng, J.: Scale invariant cosegmentation for image groups. In: CVPR (2011)
21. Paterson, M., Dančík, V.: Longest common subsequences. Mathematical Foundations of Computer Science 841, 127-142 (1994)
22. Sadanand, S., Corso, J.J.: Action bank: A high-level representation of activity in video. In: CVPR (2012)
23. Schindler, G., Krishnamurthy, P., Lublinerman, R., Liu, Y., Dellaert, F.: Detecting and matching repeated patterns for automatic geo-tagging in urban environments. In: CVPR (2008)
24. Scholkopf, B.: The kernel trick for distances. In: NIPS (2001)
25. Si, Z., Pei, M., Yao, B., Zhu, S.: Unsupervised learning of event and-or grammar and semantics from video. In: ICCV (2011)
26. Sivic, J., Zisserman, A.: Video Google: A text retrieval approach to object matching in videos. In: ICCV (2003)
27. Tang, K., Fei-Fei, L., Koller, D.: Learning latent temporal structure for complex event detection. In: CVPR (2012)
28. Turaga, P., Veeraraghavan, A., Chellappa, R.: Unsupervised view and rate invariant clustering of video sequences. CVIU 113(3), 353-371 (2009)
29. Viola, P., Jones, M.: Robust real-time face detection. International Journal of Computer Vision 57(2), 137-154 (2004)
30. Wang, Y., Jiang, H., Drew, M.S., Li, Z., Mori, G.: Unsupervised discovery of action classes. In: CVPR (2006)
31. Wang, Y., Velipasalar, S.: Frame-level temporal calibration of unsynchronized cameras by using Longest Consecutive Common Subsequence. In: ICASSP (2009)
32. Yuan, J., Liu, Z., Wu, Y.: Discriminative video pattern search for efficient action detection. PAMI 33(9), 1728-1743 (2011)
33. Zhou, F., De la Torre, F., Cohn, J.F.: Unsupervised discovery of facial events. In: CVPR (2010)
34. Zhu, S., Mumford, D.: A stochastic grammar of images. Foundations and Trends in Computer Graphics and Vision 2(4), 259-362 (2006)