Action categorization with modified hidden conditional random field

Pattern Recognition

Volumn 43, Issue 1, 2010, Pages 197-203

  Zhang, Jianguo ^a   Gong, Shaogang ^b  

^a QUEEN'S UNIVERSITY BELFAST   (United Kingdom)

^b QUEEN MARY UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

Action recognition; Graph model; Hidden conditional random field; Optimum learning

Indexed keywords

ACTION RECOGNITION; CHAIN CODES; GLOBAL OPTIMUM; GRAPH MODEL; HIDDEN CONDITIONAL RANDOM FIELD; HIDDEN CONDITIONAL RANDOM FIELDS; LOGISTIC REGRESSIONS; MODEL-BASED METHOD; OPTIMUM LEARNING;

CODES (SYMBOLS); HIDDEN MARKOV MODELS;

COSMIC RAYS;

EID: 68949131112     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2009.05.015     Document Type: Article

References (26)

1. M. Ahmad, S.-W. Lee, HMM-based human action recognition using multiview image sequences, in: International Conference on Pattern Recognition, 2006, pp. 263-266.
2. Belongie S., Malik J., and Puzicha J. Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence 24 4 (2002) 509-522
3. M. Blank, L. Gorelick, E. Shechtman, M. Irani, R. Basri, Actions as space-time shapes, in: International Conference on Computer Vision, 2005, pp. 1395-1402, URL: 〈www.wisdom.weizmann.ac.il/vision/SpaceTimeActions.html〉.
4. Bobick A.F., and Davis J.W. The recognition of human movement using temporal templates. IEEE Transactions on Pattern Analysis and Machine Intelligence 23 3 (2001) 257-267
5. J.R. Brzezinski, Logistic regression modeling for context-based classification, in: DEXA '99: Proceedings of the 10th International Workshop on Database & Expert Systems Applications, 1999, p. 755.
6. Chapelle O., Haffner P., and Vapnik V. Support vector machines for histogram-based image classification. IEEE Transactions on Neural Networks 10 5 (1999) 1055-1064
7. A.A. Efros, A.C. Berg, G. Mori, J. Malik, Recognizing action at a distance, in: International Conference on Computer Vision, Nice, France, 2003, pp. 726-733.
8. Figueiredo M.A.T., and Jain A.K. Unsupervised learning of finite mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence 24 3 (2002) 381-396
9. Freeman H. On encoding arbitrary geometric configurations. IRE Transactions on Electronic Computers 10 2 (1961) 260-268
10. Fukunaga K. Introduction to Statistical Pattern Recognition. second ed. (1990), Academic Press Professional, San Diego, CA, USA
11. A. Gunawardana, M. Mahajan, A. Acero, J.C. Platt, Hidden conditional random fields for phone classification, in: International Conference on Speech Communication and Technology, 2005.
12. Lee H.-K., and Kim J.H. An HMM-based threshold model approach for gesture recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 21 10 (1999)
13. Moeslunda T.B., Hiltonb A., and Krügerc V. A survey of advances in vision-based human motion capture and analysis. Computer Vision and Image Understanding 104 2-3 (2006) 90-126
14. J.C. Niebles, H. Wang, L. Fei-Fei, Unsupervised learning of human action categories using spatial-temporal words, in: British Machine Vision Conference, 2006.
15. A. Quattoni, S. Wang, L.-P. Morency, M. Collins, T. Darrell, Hidden-state conditional random fields, Technical Report, MIT, 2006.
16. Ramanan D., Forsyth D.A., and Zisserman A. Tracking people by learning their appearance. IEEE Transactions on Pattern Analysis and Machine Intelligence 29 1 (2007) 65-81
17. Rissanen J. A universal prior for integers and estimation by minimum description length. Annals of Statistics 11 2 (1983) 417-431
18. T. Matsuyama, S. Nobuhara, Y. Tsuda, I. Ohama, Multi-viewpoint silhouette extraction with 3d context-aware error detection, correction, and shadow suppression, in: Fourth European Conference on Visual Media Production (CVMP 2007), vols. 27 and 28, 2007, pp. 1-9.
19. C. Schuldt, I. Laptev, B. Caputo, Recognizing human actions: a local SVM approach, in: ICPR, Cambridge, UK, 2004, pp. 32-36.
20. Sminchisescu C., Kanaujia A., and Metaxasb D. Conditional models for contextual human motion recognition. Computer Vision and Image Understanding 104 2-3 (2006) 210-220
21. C. Stauffer, W.E.L. Grimson, Adaptive background mixture models for real time tracking, in: IEEE Conference on Computer Vision and Pattern Recognition, 1999.
22. C. Sutton, A. McCallum, An introduction to conditional random fields for relational learning, in: L. Getoor, B. Taskar (Eds.), Introduction to Statistical Relational Learning, MIT Press, Cambridge, 2006, pp. 93-128.
23. Wang L., Tan T., Ning H., and Hu W. Silhouette analysis-based gait recognition for human identification. IEEE Transactions on Pattern Analysis and Machine Intelligence 25 12 (2003)
24. S.B. Wang, A. Quattoni, L.-P. Morency, D. Demirdjian, T. Darrell, Hidden conditional random fields for gesture recognition, in: IEEE Conference on Computer Vision and Pattern Recognition, 2006, pp. 1521-1527.
25. A. Yilmaz, M. Shah, Actions sketch: a novel action representation, in: IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, USA, 2005, pp. 984-989.
26. Y. Zhuang, C. Chen, Efficient silhouette extraction with dynamic viewpoint, in: International Conference on Computer Vision, 2007, pp. 1-8.