Incremental EM for Probabilistic Latent Semantic Analysis on human action recognition

6th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2009

Volumn , Issue , 2009, Pages 55-60

  Xu, Jie ^a   Ye, Getian ^a   Wang, Yang ^a   Herman, Gunawan ^a   Zhang, Bang ^a   Yang, Jun ^a  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

Incremental EM; PLSA

Indexed keywords

ACTION RECOGNITION; AUTOMATIC UNDERSTANDING; DATA SETS; EM ALGORITHMS; EM TRAINING; EXPECTATION-MAXIMIZATION ALGORITHMS; HUMAN ACTIONS; HUMAN-ACTION RECOGNITION; INCREMENTAL EM; ITERATIVE ESTIMATION; LIKELIHOOD FUNCTIONS; LOCAL MAXIMUM; NUMBER OF ITERATIONS; PLSA MODEL; PROBABILISTIC LATENT SEMANTIC ANALYSIS; RECOGNITION ACCURACY; SPEED-UPS; TRAINING DATA; TRAINING TIME; VIDEO SURVEILLANCE;

ALGORITHMS; GESTURE RECOGNITION; IMAGE RETRIEVAL; MAXIMUM LIKELIHOOD ESTIMATION; MONITORING; PARAMETER ESTIMATION; SEMANTICS;

SECURITY SYSTEMS;

EID: 72349086409     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/AVSS.2009.66     Document Type: Conference Paper

References (15)

1. M. Blank, L. Gorelick, E. Shechtman, M. Irani, and R. Basri. Actions as Space-Time Shapes. In In Proceedings of the tenth IEEE International Conference on Computer Vision, volume 2, 2005.
2. J. Carlos Niebles, H. Wang, and F.-F. Li. Unsupervsied Learning of Human Action Categories Using Spatial-Temporal Words. Internationl Journal on Computer Vision, 42(1):993-1022, 2008.
3. A. Dempster, N. Laird, D. Rubin, et al. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, 39(1):1-38, 1977.
4. P. Dollär, R. Vincent, and C. Garrison. Behavior recognition via sparse spatio-temporal features. In VS-PETS, pages 65-72, 2005.
5. T. Hofmann. Unsupervised Learning by Probabilistic Latent Semantic Analysis. Machine Learning, 42(1):177-196, 2001.
6. N. Ikizler and D. Forsyth. Searching Video for Complex Activities with Finite State Models. In In Proceedings of the tenth IEEE International Conference on Computer Vision, volume 3, 2007.
7. Y. Ke, R. Sukthankar, and M. Hebert. Efficient visual event detection using volumetric features. In Tenth IEEE International Conference on Computer Vision, 2005.
8. I. Laptev and L. Tony. Space-Time Interest Points. International Conference on Computer Vision, 1:432-439, 2003.
9. J. Little and J. Boyd. Recognizing people by their gait: the shape of motion. Videre: Journal of Computer Vision Research, 1(2): 1-32, 1998.
10. D. M. Blei, A. Y. Ng, and M. I.Jordan. Latent Dirichlet Allocation. The Journal of Machine Learning Research, 3:993-1022, 2003.
11. R. M. Neal and G. E. Hinton. A view of the em algorithm that justifies incremental, sparse, and other variants. Learning in Graphical Models, pages 355-368, 1998.
12. I. Pruteanu-Malinici and L. Carin. Infinite hidden markov models for unusual-event detection in video. IEEE Transactions on Image Processing, 17:811-822, 2008.
13. C. Schuldt, I. Laptev, and B. Caputo. Recognizing human actions: A local svm approach. In ICPR, pages 32-36, 2004.
14. T. Xiang and S. Gong. Beyond tracking: Modelling activity and understanding behaviour. International Journal of Computer Vision, 67(1):21-51, 2006.
15. A. Yilmaz and M. Shah. Recognizing Human Actions in Videos Acquired by Uncalibrated Moving Cameras. In Tenth IEEE International Conference on Computer Vision, 2005.