Optimizing multi-graph learning: Towards a unified video annotation scheme

Proceedings of the ACM International Multimedia Conference and Exhibition

Volumn , Issue , 2007, Pages 862-871

  Wang, Meng ^a   Hua, Xian Sheng ^b   Yuan, Xun ^a   Song, Yan ^a   Dai, Li Rong ^a  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b MICROSOFT RESEARCH ASIA   (China)

Author keywords

Multimodal fusion; Semi supervised learning; Video annotation

Indexed keywords

GRAPH THEORY; IMAGE ANALYSIS; OPTIMIZATION;

MULTIMODAL FUSION; SEMI-SUPERVISED LEARNING; TEMPORAL CONSISTENCY; VIDEO ANNOTATION;

SUPERVISED LEARNING;

EID: 37849049900     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1291233.1291431     Document Type: Conference Paper

References (33)

1. LSCOM lexicon definitions and annotations version 1.0. dto challenge workshop on large scale concept ontology for multimedia. In DTO Challenge Workshop on Large Scale Concept Ontology for Multimedia, Columbia University ADVENT Technical Report #217-2006-03.
2. TREC-10 proceedings appendix on common evaluation measures, http://trec.nist.gov/pubs/trec10/appendices/measures.pdf.
3. TRECVID: TREC video retrieval evaluation, http://www-nlpir.nist.gov/ projects/trecvid.
4. C. C. Aggarwal, A. Hinneburg, and D. A. Keim. On the surprising behavior of distance metrics in high dimensional space. In Proceedings of International Conf. on Database Theory, 2001.
5. M. F. Balean, A. Blum, P. P. Choi, J. Lafferty, B. Pantano, M. R. Rwebangira, and X. Zhu. Person identification in webcam images: an application of semi-supervised learning. In Proceedings of International Conf. on Machine Learning Workshop on Learning from Partially Classified Training Data, 2005.
6. K. Beyer, J. Goldstein, and U. Shaft. When is nearest neighbor meaningful? In Proceedings of International Conf. on Database Theory, 1999.
7. O. Chapelle, A. Zien, and B. Scholkopf. Semi-Supervised Learning. MIT Press, 2006.
8. S. L. Feng, R. Manmatha, and V. Lavrenko. Multiple bernoulli relevance models for image and video annotation. In Proceedings of International Conf. on Computer Vision and Pattern Recognition, 2004.
9. A. Ghoshal, P. Arcing, and S. Khudanpur. Hidden markov models for automatic annotation and content-based retrieval of images and video. In Proceedings of International ACM SIGIR Conference, 2005.
10. A. G. Hauptmann. Lessons for the future from a decade of informedia video analysis research. In Proceedings of ACM International Conf. on Image and Video Retrieval, 2005.
11. J. R. He, M. J. Li, H. J. Zhang, H. H. Tong, and C. S. Zhang. Manifold-ranking based image retrieval. In Proceedings of ACM Multimedia, 2004.
12. J. Jeon, V. Lavrenko, and R. Manmatha. Automatic image annotation and retrieval using cross-media relevance models. In Proceedings of International ACM SIGIR Conference, 2003.
13. J. R. Kender and M. R. Naphade. Video news shot labeling refinement via shot rhythm models. In Proceedings of ICME, 2003.
14. C. Y. Lin, M. Naphade., A. Natsev, C. Neti, J. R. Smith, H. J. N. B. Tseng, and W. Adams. User-trainable video annotation using multimodal cues. In Proceedings of International ACM SIGIR Conference, 2003.
15. J. Magalhães and S. Rüger. Mining multimedia salient concepts for incremental information extraction. In Proceedings of International ACM SIGIR Conference, 2005.
16. X. Mu. Content-based video retrieval: Does video's semantic visual feature matter? In Proceedings of International ACM SIGIR Conference, 2006.
17. M. R. Naphade and J. R. Smith. On the detection of semantic concepts at TRECVID. In Proceedings of ACM Multimedia, 2004.
18. N. Sebe, M. S. Lew, and D. P. Huijsmans. Toward improved ranking metrics. IEEE trans. on Pattern Analysis and Machine Intelligence, 2000.
19. C. G. Snoek, M. Worring, and A. W. Smeulders. Early versus late fusion in semantic video analysis. In Proceedings of ACM Multimedia, 2005.
20. Y. Song, X. S. Hua, L. R. Dai, and M. Wang. Semi-automatic video annotation based on active learning with multiple complementary predictors. In Proceedings of ACM SIGMM International Workshop on Multimedia Information Retrieval, 2005.
21. M. Strieker and M. Orengo. Similarity of color images. In Proceedings of Storage and Retrieval for Image and Video Databases (SPIE 2420), 2000.
22. H. Tong, J. R. He, M. J. Li, C. S. Zhang, and W. Y. Ma. Graph-based multi-modality learning. In Proceedings of ACM Multimedia, 2005.
23. M. Wang, X. S. Hua, X. Yuan, Y. Song, and L. R. Dai. Multi-graph semi-supervised learning for video semantic feature extraction. In Proceedings of ICME, 2007.
24. M. Wang, X. S. Hua, X. Yuan, Y. Song, S. Li, and H. J. Zhang. Automatic video annotation by semi-supervised learning with kernel density estimation. In Proceedings of ACM Multimedia, 2006.
25. Y. Wu, E. Y. Chang, K. C. C. Chang, and J. R. Smith. Optimal multimodal fusion for multimedia data analysis. In Proceedings of ACM Multimedia, 2004.
26. R. Yan and A. G. Hauptmann. The combination limit in multimedia retrieval. In Proceedings of ACM Multimedia, 2003.
27. R. Yan and M. R. Naphade. Semi-supervised cross feature learning for semantic concept detection in videos. In Proceedings of International Conf. on Computer Vision and Pattern Recognition, 2005.
28. J. Yang and A. G. Hauptmann. Exploring temporal consistency for video analysis and retrieval. In Proceedings of ACM SIGMM International Workshop on Multimedia Information Retrieval, 2006.
29. J. Yu, J. Amores, N. Sebe, , and Q. Tian. Toward robust distance metric analysis for similarity estimation. In Proceedings of International Conf. on Computer Vision and Pattern Recognition, 2006.
30. X. Yuan, X. S. Hua, M. Wang, and X. Wu. Manifold-ranking based video concept detection on large database and feature pool. In Proceedings of ACM Multimedia, 2006.
31. D. Zhou, O. Bousquet, T. N. Lal, J. Weston, and B. Scholkopf. Learning with local and global consistency. In Proceedings of Advances of Neural Information Processing, 2004.
32. X. Zhu. Semi-supervised learning literature survey. Technical Report (1530), University of Wisconsin-Madison.
33. X. Zhu. Semi-supervised learning with graphs. Doctoral thesis, CMU-LTI-05-192, 2005.