Which Faces to Tag: Adding Prior Constraints into Active Learning

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2009, Pages 1058-1065

  Kapoor, Ashish ^a   Hua, Gang ^b   Akbarzadeh, Amir ^b   Baker, Simon ^a  

^a MICROSOFT RESEARCH   (United States)

^b MICROSOFT   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER VISION; ITERATIVE METHODS; MESSAGE PASSING; PROBABILITY DISTRIBUTIONS; UNCERTAINTY ANALYSIS;

ACTIVE LEARNING; DISCRIMINATIVE MODELS; KNOW-THAT; LABEL INFORMATION; LEARNING PARADIGMS; MESSAGE-PASSING; PERSONAL PHOTO COLLECTION; POSTERIOR DISTRIBUTIONS; PROBABILISTICS; SINGLE-TRACKS;

FACE RECOGNITION;

EID: 84860189970     PISSN: 15505499     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2009.5459392     Document Type: Conference Paper

References (21)

1. http://picasaweb.google.com/.
2. http://www.apple.com/ilife/iphoto/.
3. Y. Abramson and Y. Freund. Active learning for visual object recognition. Technical report, UCSD, 2004.
4. E. Y. Chang, S. Tong, K. Goh, and C. Chang. Support vector machine concept-dependent active learning for image retrieval. IEEE Transactions on Multimedia, 2005.
5. J. Cui, F. Wen, R. Xiao, Y. Tian, and X. Tang. Easyalbum: an interactive photo annotation system based on face clustering and re-ranking. In CHI, 2007.
6. Y. Freund, H. S. Seung, E. Shamir, and N. Tishby. Selective sampling using the query by committee algorithm. Machine Learning, 28(2-3), 1997.
7. A. Holub, P. Perona, and M. Burl. Entropy-based active learning for object recognition. In CVPR workshop on Online Learning for Classification, 2008.
8. T. Kanade. Picture Processing by Computer Complex and Recognition of Human Faces. PhD thesis, Kyoto University, 1973.
9. A. Kapoor, K. Grauman, R. Urtasun, and T. Darrell. Active learning with Gaussian Processes for object categorization. In ICCV, 2007.
10. N. Lawrence, M. Seeger, and R. Herbrich. Fast sparse Gaussian Process method: Informative vector machines. NIPS, 2002.
11. D. MacKay. Information-based objective functions for active data selection. Neural Computation, 4(4), 1992.
12. P. J. Phillips, P. J. Flynn, T. Scruggs, K. W. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min, and W. Worek. Overview of the face recognition grand challenge. In CVPR, 2009.
13. C. E. Rasmusen and C. Williams. Gaussian Processes for Machine Learning. MIT Press, 2006.
14. T. Sim, S. Baker, and M. Bsat. The cmu pose, illumination, and expression database. PAMI, 25(12), 2003.
15. Y. Tian, W. Liu, R. Xiao, F. Wen, and X. Tang. A face annotation framework with partial clustering and interactive labeling. In CVPR, 2007.
16. S. Tong and D. Koller. Support vector machine active learning with applications to text classification. In ICML, 2000.
17. M. Turk and A. Pentland. Face recognition using eigenfaces. In CVPR, 1991.
18. P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. In CVPR, 2001.
19. J. Wright and G. Hua. Implicit elastic matching with randomized projections for pose-variant face recognition. In CVPR, 2009.
20. R. Yan, J. Yang, and A. Hauptmann. Automatically labeling video data using multi-class active learning. In ICCV, 2003.
21. W. Zhao, R. Chellappa, J. Phillips, and A. Rosenfeld. Face recognition: A literature survey. ACM Computing Surveys, pages 399-458, 2003.