Ensemble multi-instance multi-label learning approach for video annotation task

MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops

Volumn , Issue , 2011, Pages 1153-1156

  Xu, Xin Shun ^a   Xue, Xiangyang ^b   Zhou, Zhi Hua ^c  

^a NANJING UNIVERSITY   (China)

^b SHANDONG UNIVERSITY   (China)

^c FUDAN UNIVERSITY   (China)

Author keywords

Ensemble methods; Multi instance multi label learning; Video annotation

Indexed keywords

AUTOMATIC VIDEO ANNOTATION; CLASS IMBALANCE; DATA SETS; ENSEMBLE METHODS; FEATURE VECTORS; LEARNING APPROACH; LEARNING PROBLEM; MULTI-LABEL; NATURAL STRUCTURES; STATE-OF-THE-ART METHODS; TEMPORAL CONSISTENCY; TRECVID; VIDEO ANNOTATIONS; VIDEO CLIPS; VIDEO DATA; VIDEO INDEXING;

STRUCTURE (COMPOSITION); VIDEO CAMERAS; VIDEO RECORDING;

IMAGE RETRIEVAL;

EID: 84455205895     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2072298.2071962     Document Type: Conference Paper

References (27)

1. A. Amir, M. Berg, and S.-F. Chang. IBM research TRECVID-2003 video retrieval system. In TREVID, 2003.
2. T. G. Dietterich, R. H. Lathrop, and T. Lozano-Pérez. Solving the multiple-instance problem with axis-parallel rectangles. Artificial Intelligence, 89(1-2):31-71, 1997.
3. T. Gartner, P. A. Flach, A. Kowalczyk, and A. J. Smola. Multi-instance kernels. In ICML, 179-186, 2002.
4. A. Ghoshal, P. Ircing, and S. Khudanpur. Hidden markov models for automatic annotation and content-based retrieval of images and video. In SIGIR, 544-551, 2005.
5. K. Grauman and T. Darrell. The pyramid match kernel: Efficient learning with sets of features. Journal of Machine Learning Research, 8:725-760, 2007.
6. Z. Gu, T. Mei, X.-S. Hua, J. Tang, and X. Wu. Multi-layer multi-instance learning for video concept detection. IEEE Transactions on Multimedia, 10(8):1605-1616, 2008.
7. R. Jin, S. Wang, and Z.-H. Zhou. Learning a distance metric from multi-instance multi-label data. In CVPR, 896-902, 2009.
8. Y.-X. Li, S. Ji, J. Ye, and Z.-H. Zhou. Drosophila gene expression pattern annotation through multi-instance multi-label learning. In IJCAI, 1445-1450, 2009.
9. X.-Y. Liu, J. Wu, and Z.-H. Zhou. Exploratory undersampling for class-imbalance learning. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 39(2):539-550, 2009.
10. M. Naphade, L. Kennedy, J. R. Kender, S. F. Chang, P. Over, and A. Hauptmann. A light scale concept ontology for multimedia understanding for TRECVID 2005. In IBM Research Technical Report, 2005.
11. M. Naphade and J. Smith. A generalized multiple instance learning algorithm for large scale modeling of multimedia semantics. In ICASSP, 341-344, 2005.
12. M. R. Naphade, I. Kozintsev, and T. Huang. Factor graph framework for semantic video indexing. IEEE Transactions on Circuits and Systems for Video Technology, 12(1):40-52, 2002.
13. N. Nguyen. A new SVM approach to multi-instance multi-label learning. In ICDM, 384-392, 2010.
14. G.-J. Qi, X.-S. Hua, Y. Rui, J. Tang, T. Mei, M. Wang, and H.-J. Zhang. Correlative multilabel video annotation with temporal kernels. ACM Transactions on Multimedia Computing, Communications and Applications, 5(1):1-27, 2008.
15. J. R. Smith, M. Naphade, and A. Natsev. Multimedia semantic indexing using model vectors. In ICME, 445-448, 2003.
16. 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 MM, 421-430, 2006.
17. J. Tang, X.-S. Hua, T. Mei, G.-J. Qi, S. Li, and X. Wu. Temporally consistent gaussian random field for video semantic analysis. In ICIP, 525-528, 2007.
18. K. E. van de Sande, T. Gevers, and C. G. Snoek. Evaluating color descriptors for object and scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(9):1582-1596, 2010.
19. J. Yang and A. G. Hauptmann. Exploring temporal consistency for video analysis and retrieval. In MIR, 33-42, 2006.
20. S.-H. Yang, H. Zha, and B.-G. Hu. Dirichlet-bernoulli alignment: A generative model for multi-class multi-label multi-instance corpora. In NIPS 23, 2143-2150, 2009.
21. J. Yuan, Z. Guo, and L. Lv, et al. THU and ICRC at TRECVID. In TRECVID, 2007.
22. Z.-J. Zha, X.-S. Hua, T. Mei, J. Wang, G.-J. Qi, and Z. Wang. Joint multi-label multi-instance learning for image classification. In CVPR, 1-8, 2008.
23. M.-L. Zhang and Z.-H. Zhou. M3MIML: A maximum margin method for multi-instance multi-label learning. In ICDM, 688-697, 2008.
24. Z.-H. Zhou. Ensemble learning. In S. Z. Li, editor, Encyclopedia of Biometrics, pages 270-273. Springer, 2009.
25. Z.-H. Zhou, Y.-Y. Sun, and Y.-F. Li. Multi-instance learning by treating instances as non-i.i.d. samples. In ICML, 1249-1256, 2009.
26. Z.-H. Zhou and M.-L. Zhang. Multi-instance multi-label learning with application to scene classification. In NIPS 19, 1609-1616, 2007.
27. Z.-H. Zhou, M.-L. Zhang, S.-J. Huang, and Y.-F. Li. MIML: A framework for learning with ambiguous objects. CoRR abs/0808.3231, 2008.