Kernelized Multiview Projection for Robust Action Recognition

International Journal of Computer Vision

Volumn 118, Issue 2, 2016, Pages 115-129

  Shao, Ling ^a   Liu, Li ^a   Yu, Mengyang ^a  

^a NORTHUMBRIA UNIVERSITY   (United Kingdom)

Author keywords

Dimensionality reduction; Human action recognition; Multiple view fusion; Sequential distance learning; Spectral coding

Indexed keywords

ALGORITHMS; DISTANCE EDUCATION; GESTURE RECOGNITION;

ACTION RECOGNITION ALGORITHMS; DIMENSIONALITY REDUCTION; FEATURE REPRESENTATION; HUMAN-ACTION RECOGNITION; MULTIPLE VIEWS; REPRODUCING KERNEL HILBERT SPACES; SPECTRAL CODING; STATE-OF-THE-ART TECHNIQUES;

MOTION ESTIMATION;

EID: 84944707196     PISSN: 09205691     EISSN: 15731405     Source Type: Journal    
DOI: 10.1007/s11263-015-0861-6     Document Type: Article

References (54)

1. Ahonen, T., Hadid, A., & Pietikäinen, M. (2004). Face recognition with local binary patterns. In European Conference on Computer Vision, Prague.
2. Belkin, M. & Niyogi, P. (2001). Laplacian eigenmaps and spectral techniques for embedding and clustering. In Advances in Neural Information Processing Systems. New York: MIT Press.
3. Berndt, D. J., & Clifford, J. (1994). Using dynamic time warping to find patterns in time series. KDD Workshop, 10, 359–370.
4. Bezdek, J. C. & Hathaway, R. J. (2002). Some notes on alternating optimization. In AFSS International Conference on Fuzzy Systems, Calcutta.
5. Bhatia, R. (1997). Matrix analysis. New York: Springer.
6. Bhattacharya, S., Sukthankar, R., Jin, R., & Shah, M. (2011). A probabilistic representation for efficient large scale visual recognition tasks. In IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs.
7. Bickel, S. & Scheffer, T. (2004). Multi-view clustering. In International Conference on Data Mining, Brighton.
8. Bobick, A. F., & Davis, J. W. (2001). The recognition of human movement using temporal templates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(3), 257–267.
9. Brendel, W., & Todorovic, S. (2010). Activities as time series of human postures. In European Conference on Computer Vision, Heraklion.
10. Dalal, N. & Triggs, B. (2005). Histograms of oriented gradients for human detection. In IEEE Conference on Computer Vision and Pattern Recognition, San Diego.
11. Dollár, P., Rabaud, V., Cottrell, G., & Belongie, S. (2005). Behavior recognition via sparse spatio-temporal features. In: 2nd joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (pp. 65–72).
12. Gilbert, A., Illingworth, J., & Bowden, R. (2011). Action recognition using mined hierarchical compound features. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(5), 883–897.
13. Gönen, M., & Alpaydin, E. (2011). Multiple kernel learning algorithms. Journal of Machine Learning Research, 12, 2211–2268.
14. Hardy, G. H., Littlewood, J. E., & Pólya, G. (1952). Inequalities. Cambridge: Cambridge University Press.
15. He, X. & Niyogi, P. (2004). Locality preserving projections. In Advances in Neural Information Processing Systems, New York.
16. Jhuang, H., Serre, T., Wolf, L., & Poggio, T. (2007). A biologically inspired system for action recognition. In International Conference on Computer Vision. New York: IEEE Press.
17. Ji, S., Xu, W., Yang, M., & Yu, K. (2013). 3d Convolutional neural networks for human action recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(1), 221–231.
18. Jiang, Y. G., Dai, Q., Xue, X., Liu, W., & Ngo, C. W. (2012). Trajectory-based modeling of human actions with motion reference points. In European Conference on Computer Vision, Florence.
19. Kihl, O., Picard, D., & Gosselin, P. H. (2015). A unified framework for local visual descriptors evaluation. Pattern Recognition, 48(4), 1174–1184.
20. Klaser, A. & Marszalek, M. (2008). A spatio-temporal descriptor based on 3d-gradients. In British Machine Vision Conference, Leeds.
21. Kovashka, A. & Grauman, K. (2010). Learning a hierarchy of discriminative space-time neighborhood features for human action recognition. In IEEE Conference on Computer Vision and Pattern Recognition, Singapore.
22. Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., & Serre, T. (2011). Hmdb: A large video database for human motion recognition. In: IEEE International Conference on Computer Vision.
23. Laptev, I., Marszalek, M., Schmid, C., & Rozenfeld, B. (2008). Learning realistic human actions from movies. In IEEE Conference on Computer Vision and Pattern Recognition, New York.
24. Le, Q. V., Zou, W. Y., Yeung, S. Y., & Ng, A. Y. (2011). Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis. In IEEE Conference on Computer Vision and Pattern Recognition, New York.
25. Liu, J., Luo, J., & Shah, M. (2009). Recognizing realistic actions from videos in the wild. In IEEE Conference on Computer Vision and Pattern Recognition, Leeds.
26. Liu, L., Shao, L., Li, X., & Lu, K. (2015). Learning spatio-temporal representations for action recognition: A genetic programming approach. IEEE Transactions on Cybernetics. doi:10.1109/TCYB.2015.2399172.
27. Liu, L., Shao, L., & Rockett, P. (2013). Boosted key-frame selection and correlated pyramidal motion-feature representation for human action recognition. Pattern Recognition, 46(7), 1810–1818.
28. Long, B., Philip, S. Y., & Zhang, Z. M. (2008). A general model for multiple view unsupervised learning. In International Conference on Data Mining, New York.
29. Marszalek, M., Laptev, I., & Schmid, C. (2009). Actions in context. In IEEE Conference on Computer Vision and Pattern Recognition, New York.
30. O’Hara, S. & Draper, B. A. (2012). Scalable action recognition with a subspace forest. In IEEE Conference on Computer Vision and Pattern Recognition, New York.
31. Rodriguez, M. D., Ahmed, J., & Shah, M. (2008). Action mach a spatio-temporal maximum average correlation height filter for action recognition. In IEEE Conference on Computer Vision and Pattern Recognition, New York.
32. Sapienza, M., Cuzzolin, F., & Torr, P. H. (2012). Learning discriminative space-time actions from weakly labelled videos. In British Machine Vision Conference, San Diego.
33. Schindler, K. & Van Gool, L. (2008). Action snippets: How many frames does human action recognition require? In IEEE Conference on Computer Vision and Pattern Recognition, New York.
34. Schuldt, C., Laptev, I., & Caputo, B. (2004). Recognizing human actions: A local SVM approach. International Conference on Pattern Recognition, 3, 32–36.
35. Scovanner, P., Ali, S., & Shah, M. (2007). A 3-dimensional SIFT descriptor and its application to action recognition. In International Conference on Multimedia, Bulgaria.
36. Shao, L., Zhen, X., Tao, D., & Li, X. (2014). Spatio-temporal laplacian pyramid coding for action recognition. IEEE Transactions on Cybernetics, 44(6), 817–827.
37. Simonyan, K. & Zisserman, A. (2014). Two-stream convolutional networks for action recognition in videos. In Advances in Neural Information Processing Systems (pp. 568–576).
38. Sun, L., Jia, K., Chan, T. H., Fang, Y., Wang, G., & Yan, S. (2014). Dl-sfa: Deeply-learned slow feature analysis for action recognition. In IEEE Conference on Computer Vision and Pattern Recognition (pp. 2625–2632).
39. Tao, D., Li, X., Wu, X., & Maybank, S. J. (2007). General tensor discriminant analysis and gabor features for gait recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(10), 1700–1715.
40. Taylor, G., Fergus, R., LeCun, Y., & Bregler, C. (2010). Convolutional learning of spatio-temporal features. In European Conference on Computer Vision.
41. Vrigkas, M., Karavasilis, V., Nikou, C., & Kakadiaris, I. A. (2014). Matching mixtures of curves for human action recognition. Computer Vision and Image Understanding, 119, 27–40.
42. Wang, H., Klaser, A., Schmid, C., & Liu, C.L. (2011). Action recognition by dense trajectories. In IEEE Conference on Computer Vision and Pattern Recognition, New York.
43. Wang, H., Kläser, A., Schmid, C., & Liu, C. L. (2013). Dense trajectories and motion boundary descriptors for action recognition. International Journal of Computer Vision, 103(1), 60–79.
44. Wang, H., & Schmid, C. (2013). Action recognition with improved trajectories. In IEEE International Conference on Computer Vision, San Diego.
45. Wang, H., Ullah, M. M., Klaser, A., Laptev, I., & Schmid, C., et al. (2009). Evaluation of local spatio-temporal features for action recognition. In British Machine Vision Conference, New York.
46. Wu, X., Xu, D., Duan, L., & Luo, J. (2011). Action recognition using context and appearance distribution features. In IEEE Conference on Computer Vision and Pattern Recognition, New York.
47. Xia, T., Tao, D., Mei, T., & Zhang, Y. (2010). Multiview spectral embedding. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 40(6), 1438–1446.
48. Xu, C., Tao, D., & Xu, C. (2014). Large-margin multi-viewinformation bottleneck. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(8), 1559–1572.
49. Xu, C., Tao, D., & Xu, C. (2015). Multi-view intact space learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. doi:10.1109/TPAMI.2015.2417578.
50. Yao, A., Gall, J., & Van Gool, L. (2010). A hough transform-based voting framework for action recognition. In IEEE Conference on Computer Vision and Pattern Recognition, New York.
51. Yu, M., Liu, L., & Shao, L. (2015). Kernelized multiview projection. arXiv:1508.00430.
52. Zhang, Z., & Tao, D. (2012). Slow feature analysis for human action recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(3), 436–450.
53. Zhao, Z. & Liu, H. (2008). Multi-source feature selection via geometry-dependent covariance analysis. In Third Workshop on New Challenges for Feature Selection in Data Mining and Knowledge Discovery (pp. 36–47).
54. Zien, A. & Ong, C.S. (2007). Multiclass multiple kernel learning. In International Conference on Machine Learning, New York.