Fast human action classification and VOI localization with enhanced sparse coding

Journal of Visual Communication and Image Representation

Volumn 24, Issue 2, 2013, Pages 127-136

  Lu, Shiyang ^a   Zhang, Jian ^b   Wang, Zhiyong ^a   Feng, David Dagan ^a  

^a UNIVERSITY OF SYDNEY   (Australia)

^b UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

Author keywords

Human action classification; Localization; Sparse coding; Volume of Interest (VOI)

Indexed keywords

BRANCH AND BOUND METHOD; CLASSIFICATION (OF INFORMATION); IMAGE CODING; QUANTIZATION (SIGNAL);

BRANCH-AND-BOUND APPROACHES; DISCRIMINATIVE DICTIONARIES; HUMAN ACTION CLASSIFICATIONS; LOCALIZATION; SPARSE CODING; SPATIAL AND TEMPORAL VARIATION; STATE-OF-THE-ART METHODS; VOLUME OF INTEREST;

CODES (SYMBOLS);

EID: 84881140711     PISSN: 10473203     EISSN: 10959076     Source Type: Journal    
DOI: 10.1016/j.jvcir.2012.07.008     Document Type: Article

References (35)

1. I. Laptev, M. Marszalek, C. Schmid, B. Rozenfeld, Learning realistic human actions from movies, in: IEEE Conference on Computer Vision and Pattern Recognition, 2008.
2. J.C. Niebles, F.-F. Li, A hierarchical model of shape and appearance for human action classification, in: IEEE Conference on Computer Vision and Pattern Recognition, 2007.
3. J.C. Niebles, H. Wang, F.-F. Li, Unsupervised learning of human action categories using spatial-temporal words, International Journal of Computer Vision 79 (3) (2008) 299-318.
4. C. Schuldt, I. Laptev, B. Caputo, Recognizing human actions: a local svm approach, in: International Conference on 17th Proceedings of the Pattern Recognition, 2004, pp. 32-36.
5. L. Gorelick, M. Blank, E. Shechtman, M. Irani, R. Basri, Actions as space-time shapes, in: IEEE International Conference on Computer Vision, 2005, pp. 1395-1402. (Pubitemid 44042342)
6. J. Liu, Y. Yang, M. Shah, Learning semantic visual vocabularies using diffusion distance, in: IEEE Conference on Computer Vision and Pattern Recognition, 2009.
7. L. Cao, Z. Liu, T.S. Huang, Cross-dataset action detection, in: IEEE Conference on Computer Vision and Pattern Recognition, 2010, pp. 1998-2005.
8. J. Yang, K. Yu, T.S. Huang, Supervised translation-invariant sparse coding, in: IEEE Conference on Computer Vision and Pattern Recognition, 2010, pp. 3517-3524.
9. J. Yang, K. Yu, Y. Gong, T.S. Huang, Linear spatial pyramid matching using sparse coding for image classification., in: IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2009, pp. 1794-1801.
10. J. Tang, R. Hong, S. Yan, T.-S. Chua, G.-J. Qi, R. Jain, Image annotation by knnsparse graph-based label propagation over noisily tagged web images, ACM Transactions on Intelligent Systems and Technology 2 (2) (2011) 14:1-14:15.
11. Y. Boureau, J. Ponce, Y. LeCun, A theoretical analysis of feature pooling in vision algorithms, in: Proceedings of the International Conference on Machine learning (ICML'10), 2010.
12. E. Shechtman, M. Irani, Space-time behavior based correlation or how to tell if two underlying motion fields are similar without computing them, IEEE Transactions on Pattern Analysis and Machine Intelligence 29 (2007) 2045-2056. (Pubitemid 47423795)
13. H.J. Seo, P. Milanfar, Action recognition from one example, IEEE Transactions on Pattern Analysis and Machine Intelligence 33 (2011) 867-882.
14. J. Yuan, Z. Liu, Y. Wu, Discriminative subvolume search for efficient action detection, in: IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 2442-2449.
15. K.G. Derpanis, M. Sizintsev, K.J. Cannons, R.P. Wildes, Efficient action spotting based on a spacetime oriented structure representation, in: IEEE Conference on Computer Vision and Pattern Recognition, 2010, pp. 1990-1997.
16. C.H. Lampert, M.B. Blaschko, T. Hofmann, Beyond sliding windows: Object localization by efficient subwindow search, in: IEEE Conference on Computer Vision and Pattern Recognition, 2008.
17. P. Scovanner, S. Ali, M. Shah, A 3-dimensional sift descriptor and its application to action recognition, in: ACM Multimedia, 2007, pp. 357-360.
18. I. Laptev, On space-time interest points, International Journal of Computer Vision 64 (2005) 107-123. (Pubitemid 41314645)
19. S. Lazebnik, C. Schmid, J. Ponce, Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories, in: IEEE Conference on Computer Vision and Pattern Recognition, 2006.
20. M.D. Rodriguez, J. Ahmed, M. Shah, Action mach a spatio-temporal maximum average correlation height filter for action recognition, in: IEEE Conference on Computer Vision and Pattern Recognition, 2008.
21. Y. Ke, R. Sukthankar, M. Hebert, Event detection in crowded videos, in: IEEE International Conference on Computer Vision, 2007.
22. I. Laptev, P. Perez, Retrieving actions in movies, in: IEEE International Conference on Computer Vision, 2007.
23. G. Yu, J. Yuan, Z. Liu, Unsupervised random forest indexing for fast action search, in: IEEE Conference on Computer Vision and Pattern Recognition, 2011, pp. 865-872.
24. Y. Zhu, X. Zhao, Y. Fu, Y. Liu, Sparse coding on local spatial-temporal volumes for human action recognition, in: Proceedings of the 10th Asian Conference on Computer Vision (ACCV'10), 2010, pp. 660-671.
25. H. Lee, A. Battle, R. Raina, A.Y. Ng, Efficient sparse coding algorithms, Advances in Neural Information Processing Systems 19 (2007) 801-808.
26. I. Ramírez, P. Sprechmann, G. Sapiro, Classification and clustering via dictionary learning with structured incoherence and shared features, in: IEEE Conference on Computer Vision and Pattern Recognition, 2010, pp. 3501-3508.
27. F.H. Hamker, Predictions of a model of spatial attention using sum-and maxpooling functions, Neurocomputing 56 (2004) 329-343. (Pubitemid 38145631)
28. R.C. Gonzalez, R.E. Woods, Digital Image Processing, third ed., Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 2006.
29. M.F. Duarte, M.A. Davenport, D. Takhar, J.N. Laska, T. Sun, K.F. Kelly, R.G. Baraniuk, Single-pixel imaging via compressive sampling, IEEE Signal Processing Magazine 25 (2) (2008) 83-91.
30. J. Liu, J. Luo, M. Shah, Recognizing realistic actions from videos, in: IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 1996-2003.
31. R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, C.-J. Lin, A library for large linear classification, Journal of Machine Learning Research 9 (2008) 1871-1874.
32. J. Liu, M. Shah, Learning human actions via information maximization, in: IEEE Conference on Computer Vision and Pattern Recognition, 2008.
33. A. Kovashka, K. Grauman, Learning a hierarchy of discriminative space-time neighborhood features for human action recognition, in: IEEE Conference on Computer Vision and Pattern Recognition, 2010.
34. A. Yao, J. Gall, L.J.V. Gool, A hough transform-based voting framework for action recognition, in: IEEE Conference on Computer Vision and Pattern Recognition, 2010.
35. M. Bregonzio, J. Li, S. Gong, T. Xiang, Discriminative topics modelling for action feature selection and recognition, in: British Machine Vision Conference, BMVC 2010, 2010, pp. 1-11.