Video annotation through search and graph reinforcement mining

IEEE Transactions on Multimedia

Volumn 12, Issue 3, 2010, Pages 184-193

  Moxley, Emily ^a   Mei, Tao ^b   Manjunath, B S ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b MICROSOFT RESEARCH ASIA   (China)

Author keywords

Data mining; Graph theory; Video annotation; Video content analysis

Indexed keywords

MINING TECHNIQUES; MULTIMEDIA DOCUMENTS; ONLINE MEDIA; REINFORCEMENT METHOD; SEMI-SUPERVISED; TRAINING DATA; VIDEO ANNOTATIONS; VIDEO-CONTENT ANALYSIS; YOUTUBE;

GRAPH THEORY; VISUAL COMMUNICATION;

REINFORCEMENT;

EID: 77949676020     PISSN: 15209210     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMM.2010.2041101     Document Type: Article

References (36)

1. R. A. Baeza-Yates and B. A. Ribeiro-Neto, Modern Information Retrieval. Reading, MA: ACM Press/Addison-Wesley, 1999.
2. M. Cha, H. Kwak, P. Rodriguez, Y.-Y. Ahn, and S. Moon, "I tube, you tube, everybody tubes: Analyzing the world's largest user generated content video system," in Proc. ACM SIGCOMM Conf. Internet Measurement, New York, 2007, pp. 1-14.
3. O. Chapelle, B. Schölkopf, and A. Zien, Eds., Semi-Supervised Learning. Cambridge, MA: MIT Press, 2006.
4. J. Cho and A. Tomkins, "Guest editors' introduction: Social media and search," IEEE Internet Comput., vol.11, no.6, pp. 13-15, Nov.-Dec. 2007.
5. Flickr. [Online]. Available: http://www.flickr.com/.
6. G. W. Furnas, C. Fake, L. von Ahn, J. Schachter, S. Golder, K. Fox, M. Davis, C. Marlow, and M. Naaman, "Why do tagging systems work?," in Proc. Conf. Human Factors in Computing, 2006, pp. 36-39.
7. G. Geisler and S. A. Burns, "Tagging video: Conventions and strategies of theYouTube community," in Proc. Int. Conf. Digital Libraries, 2007, p. 480.
8. S. Golder and B. A. Huberman, The Structure of Collaborative Tagging Systems, Information Dynamics Lab, HP Labs, Tech. Rep., Aug. 2005.
9. Google Image Labeler. [Online]. Available: http://images.google.com/ imagelabeler/.
10. Y. Jing and S. Baluja, "Pagerank for product image search," in Proc. Int. World Wide Web Conf. (WWW 2008).
11. L. Kennedy, M. Naaman, S. Ahern, R. Nair, and T. Rattenbury, "How Flickr helps us make sense of the world: Context and content in community-contributed media collections," in Proc. ACM Multimedia, New York, 2007, pp. 631-640, ACM.
12. V. Lavrenko, S. L. Feng, and R. Manmatha, "Statistical models for automatic video annotation and retrieval," in Proc. ICASSP, 2004.
13. A. Leon-Garcia, Probability and Random Processes for Electrical Engineering. Englewood Cliffs, NJ: Prentice-Hall, 1993.
14. C. Marlow, M. Naaman, D. Boyd, and M. Davis, "HT06, tagging paper, taxonomy, Flickr, academic article, to read," in Proc. Hypertext and Hypermedia. New York: ACM Press, 2006, pp. 31-40.
15. T. Mei et al., "MSRA-USTC-SJTU at TRECVID 2007: High-level feature extraction and search," in Proc. TRECVID 2007 Workshop, Nov. 2007.
16. E. Moxley, J. Kleban, and B. Manjunath, "SpiritTagger: A geo-aware tag suggestion tool mined from Flickr," in Proc. ACM Multimedia Information Retrieval, Oct. 2008.
17. E. Moxley, T. Mei, X.-S. Hua, W.-Y. Ma, and B. Manjunath, "Automatic video annotation through search and mining," in Proc. ICME, Jun. 2008.
18. A. P. Natsev, M. R. Naphade, and J. R. Smith, "Semantic representation: Search and mining of multimedia content," in Proc. SIGKDD 2004, ACM, 2004, pp. 641-646.
19. G.-J. Qi, X.-S. Hua, Y. Rui, T. Mei, J. Tang, and H.-J. Zhang, "Concurrent multiple instance learning for image categorization," in Proc. CVPR, 2007.
20. X. Rui, M. Li, Z. Li, W.-Y. Ma, and N. Yu, "Bipartite graph reinforcement model for web image annotation," in Proc. ACM Multimedia, 2007.
21. C. Shirky, Ontology is Overrated-Categories, Links, and Tags. [Online]. Available: http://shirky.com/writings/ontology overrated.html.
22. Y. Song, X.-S. Hua, L.-R. Dai, M. Wang, and R.-H. Wang, "An automatic video semantic annotation scheme based on combination of complementary predictors," in Proc. ICASSP, 2006.
23. H. Tong, J. He, M. Li, C. Zhang, and W.-Y. Ma, "Graph based multimodality learning," in Proc. ACM Multimedia, 2005.
24. TRECVID Retrieval Evaluation. [Online]. Available: http://trecvid. nist.gov/.
25. V. S. Tseng, J.-H. Su, J.-H. Huang, and C.-J. Chen, "Integrated mining of visual features, speech features, and frequent patterns for semantic video annotation," IEEE Trans. Multimedia, vol.10, no.2, pp. 260-267, Feb. 2008.
26. A. Velivelli and T. S. Huang, "Automatic video annotation by mining speech transcripts," in Proc. CVPR Workshop, 2006.
27. A. Villacorta, "Spheres of influence," in Proc. ACM SIGGRAPH 2007, New York, 2007, p. 254.
28. 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.
29. X.-J. Wang, L. Zhang, F. Jing, and W.-Y. Ma, "Annosearch: Image auto-annotation by search," in Proc. CVPR, 2006, pp. 1483-1490.
30. X. Wu, A. Hauptmann, and C.-W. Ngo, "Novelty detection for crosslingual news stories with visual duplicates and speech transcripts," in Proc. ACM Multimedia, 2007, pp. 168-177.
31. E. P. Xing, R. Yan, and A. G. Hauptmann, "Mining associated text and images with dual-wing harmoniums," in Proc. UAI, 2005, pp. 633-641.
32. R. Yan, J. Tesic, and J. R. Smith, "Model-shared subspace boosting for multi-label classification," in Proc. SIGKDD 2007, ACM, 2007.
33. YouTube. [Online]. Available: http://www.youtube.com/.
34. D. Zhou, O. Bousquet, T. N. Lal, J. Weston, and B. Schölkopf, "Learning with local and global consistency," in Advances in Neural Information Processing Systems 16. Cambridge, MA: MIT Press, 2004.
35. X. Zhu, "Semi-supervised learning with graphs," Ph.D. dissertation, Carnegie Mellon Univ., Pittsburgh, PA, 2005.
36. X. Zhu, Semi-Supervised Learning Literature Survey, Univ. Wisconsin, Madison, Tech. Rep., 2005.