Discriminative tag learning on YouTube videos with latent sub-tags

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2011, Pages 3217-3224

  Yang, Weilong ^a   Toderici, George ^b  

^a SIMON FRASER UNIVERSITY   (Canada)

^b GOOGLE INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

SEMANTICS;

CONTENT-BASED; LARGE SCALE TESTING; LATENT INFORMATION; LATENT LEARNING; LEARNING PROCESS; LOGITBOOST; TRAINING SETS; YOUTUBE;

PATTERN RECOGNITION;

EID: 80052898069     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2011.5995402     Document Type: Conference Paper

References (26)

1. H. Aradhye, G. Toderici, and J. Yagnik. Video2text: Learning to annotate video content. In ICDM workshop, 2009.
2. S. Baluja, R. Seth, D. Sivakumar, Y. Jing, J. Yagnik, S. Kumar, D. Ravichandran, and M. Aly. Video suggestion and discovery for YouTube: taking random walks through the view graph. In WWW, 2008.
3. D. Borth, A. Ulges, and T. Breuel. Relevance filtering meets active learning: improving web-based concept detectors. In ACM MIR, 2010.
4. G. Chechik, E. Ie, M. Rehn, S. Bengio, and D. Lyon. Large-scale content-based audio retrieval from text queries. In ACM MIR, 2008.
5. N. Dalal and B. Triggs. Histogram of oriented gradients for human detection. In CVPR, 2005.
6. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. ImageNet: A Large-Scale Hierarchical Image Database. In CVPR, 2009.
7. P. Dollár, V. Rabaud, G. Cottrell, and S. Belongie. Behavior recognition via sparse spatio-temporal features. In ICCV-VS, 2005.
8. J. Fan, Y. Shen, N. Zhou, and Y. Gao. Harvesting large-scale weaklytagged image databases from the web. In CVPR, 2010.
9. P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained part based models. IEEE PAMI, 2009.
10. I. Guyon and A. Elisseeff. An introduction to variable and feature selection. JMLR, 2003.
11. S. Hwang and K. Grauman. Reading between the lines: Object localization using implicit cues from image tags. In CVPR, 2010.
12. L. Jie and F. Orabona. Learning from Candidate Labeling. In NIPS, 2010.
13. T. Leung and J. Malik. Representing and recognizing the visual appearance of materials using three-dimensional textons. IJCV, 2001.
14. L.-J. Li, R. Socher, and L. Fei-Fei. Towards total scene understanding: Classification, annotation and segmentation in an automatic framework. In CVPR, 2009.
15. R. Lyon, M. Rehn, S. Bengio, T. Walters, and G. Chechik. Sound retrieval and ranking using sparse auditory representations. Neural computation, 2010.
16. Trecvid notebook papers. http://www-nlpir.nist.gov/projects/tvpubs/tv. pubs.org.html, 2010.
17. D. Nister and H. Stewenius. Scalable recognition with a vocabulary tree. In CVPR, 2006.
18. R. Schapire. The boosting approach to machine learning: An overview. In MSRI workshop, 2002.
19. J. Shotton, M. Johnson, and R. Cipolla. Semantic texton forests for image categorization and segmentation. In CVPR, 2008.
20. C. Snoek, K. van de Sande, O. de Rooij, B. Huurnink, J. van Gemert, J. Uijlings, J. He, X. Li, I. Everts, V. Nedovic, et al. The MediaMill TRECVID 2008 semantic video search engine. In TRECVID Workshop, 2008.
21. R. Socher and L. Fei Fei. Connecting modalities: Semi-supervised segmentation and annotation of images using unaligned text corpora. In CVPR, 2010.
22. G. Toderici, H. Aradhye, M. Pasca, L. Sbaiz, and J. Yagnik. Finding meaning on youtube: Tag recommendation and category discovery. In CVPR, 2010.
23. A. Torralba, R. Fergus, and W. T. Freeman. 80 million tiny images: a large database for non-parametric object and scene recognition. PAMI, 2008.
24. A. Ulges, M. Koch, D. Borth, and T. M. Breuel. Tubetagger - youtube-based concept detection. In ICDM Workshop, 2009.
25. P. Viola and M. Jones. Robust real-time object detection. In Workshop on SCTV, 2001.
26. Y. Wang and G. Mori. A discriminative latent model of image region and object tag correspondence. In NIPS, 2010.