Tensor-based transductive learning for multimodality video semantic concept detection

IEEE Transactions on Multimedia

Volumn 11, Issue 5, 2009, Pages 868-878

  Wu, Fei ^a   Liu, Yanan ^a   Zhuang, Yueting ^a  

^a ZHEJIANG UNIVERSITY   (China)

Author keywords

Contextual temporal associated cooccurrence (CTAC); Dimensionality reduction; Higher order SVD (HOSVD); Multimodality video semantic concept detection; TensorShot; Transductive support tensor machines (TSTM)

Indexed keywords

CONTEXTUAL TEMPORAL ASSOCIATED COOCCURRENCE (CTAC); DIMENSIONALITY REDUCTION; HIGHER-ORDER SVD (HOSVD); MULTIMODALITY VIDEO SEMANTIC CONCEPT DETECTION; TENSORSHOT; TRANSDUCTIVE SUPPORT TENSOR MACHINES (TSTM);

LABELS; SEMANTICS; SINGULAR VALUE DECOMPOSITION;

TENSORS;

EID: 68549126868     PISSN: 15209210     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMM.2009.2021724     Document Type: Article

References (26)

1. J. Yang and A. G. Hauptmann, "Exploring temporal consistency for video retrieval and analysis," in Proc. 8th ACM SIGMM Int. Workshop Multimedia Information Retrieval, 2006, pp. 33-42.
2. H. Zhang, Y. T. Zhuang, and F. Wu, "Cross-modal correlation learning for clustering on image-audio dataset," in Proc. ACM Multimedia, 2007, pp. 273-276.
3. Y. Yang, Y. T. Zhuang, F. Wu, and Y. H. Pan, "Harmonizing hierarchical manifolds for multimedia document semantics understanding and cross-media retrieval," IEEE Trans. Multimedia, vol. 10, no. 3, pp. 437-446, Apr. 2008.
4. H. C. Yu and M. Bennamoun, "1D-PCA, 2D-PCA to nD-PCA," in Proc. 18th Int. Conf. Pattern Recognition (ICPR'06).
5. Y. N. Liu and F. Wu, "Video semantic concept detection using multi-modality subspace correlation propagation," in Proc. 13th Int. Multimedia Modeling Conf. (mmm2007), Singapore, 2006.
6. Y. N. Liu, F. Wu, Y. T. Zhuang, and J. Xiao, "Active post-refined multi-modality video semantic concept detection with tensor representation, " in Proc. ACM Multimedia, 2008, pp. 91-100.
7. L. Itti, C. Koch, and E. Niebur, "A modelof saliency-based visual attention for rapid scene analysis," IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 9, pp. 1075-1088, Sep. 2003.
8. L. D. Lathauwer and J. Castaing, "Tensor-based techniques for blind separation of DS-CDMA signals," Signal Processing, vol. 87, no. 2, pp. 322-336, Feb. 2007.
9. N. Liu et al., "Text representation: From vector to tensor," in Proc. Fifth IEEE Int. Conf. Data Mining (ICDM'05).
10. L. D. Lathauwer, "Signal processing based on multilinear algebra," Ph.D. dissertation, K.U.Leuven, Leuven, Belgium, 1997.
11. F. R. K. Chung, "Spectral graph theory," in Regional Conf. Series Mathematics, 1997, vol. 92.
12. M. Belkin and P. Niyogi, "Laplacian eigenmaps and spectral techniques for embedding and clustering," in Proc. Advances in Neural Information Processing Systems (NIPS2002), 2002, pp. 585-591.
13. X. F. He and P. Niyogi, "Locality preserving projections," in Proc. Advances in Neural Information Processing Systems (NIPS2003), 2003.
14. X. F. He and P. Niyogi, "Tensor subspace analysis," in Proc. Advances in Neural Information Processing Systems (NIPS2005), 2005.
15. L. D. Lathauwer, B. D. Moor, and J. Vandewalle, "A multilinear singular value decomposition," SIAM J. Matrix Anal. Appl., pp. 1253-1278, 2000.
16. D. C. Tao, X. L. Li, X. D. Wu, W. M. Hu, and S. J. Maybox, "Supervised tensor learning," in Proc. IEEE Conf. Data Mining (ICDM), 2005, pp. 450-457.
17. X. J. Zhu, "Semi-Supervised Learning Literature Survey" , Univ. Wisconsin-Madison, Comput. Sci., Jun. 2007. [Online]. Available: http:// pages.cs.wisc.edu/∼jerryzhu/research/ssl/semireview.html.
18. R. Yan and M. Naphade, "Semi-supervised cross feature learning for semantic concept detection in videos," in Proc. Computer Vision and Pattern Recognition (CVPR), 2005, pp. 657-663.
19. M. Wang, X. S. Hua, X. Yuan, Y. Song, and L. R. Dai, "Optimizing multi-graph learning: Towards a unified video annotation scheme," in Proc. ACM Multimedia, 2007, pp. 862-871.
20. T. Joachims, "Transductive inference for text classification using support vector machines," in Proc. 16th Int. Conf. Machine Learning (ICML), San Francisco, 1999, pp. 200-209.
21. G. Dai and D. Y. Yeung, "Tensor embedding methods," in Proc. 21st National Conf. Artificial Intelligence (AAAI'06).
22. B. W. Bader and T. G. Kolda, Efficient MATLAB Computations With Sparse and Factored Tensors, Sandia National Laboratories, Tech. Rep. SAND02006-7592, Dec. 2006.
23. TREVID. [Online]. Available: http://www-nlpir.nist.gov/projects/ trevid/.
24. China-US Million Book Digital Library Project. [Online]. Available: http://www.cadal.zju.edu.cn.
25. C. G. M. Snoek, M. Worring, J. C. van Gemert, J. M. Geusebroek, and A. W. M. Smeulders, "The challenge problem for automated detection of 101 semantic concepts in multimedia," in Proc. ACM Multimedia, 2006, pp. 421-430.
26. G. J. Qi et al., "Correlative multi-label video annotation," in Proc. ACM Multimedia, 2007, pp. 17-26.