Meta-tag propagation by co-training an ensemble classifier for improving image search relevance

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops

Volumn , Issue , 2008, Pages

  Sharma, Aayush ^a,b   Hua, Gang ^b   Liu, Zicheng ^b   Zhang, Zhcngyou ^b  

^a INDIAN INSTITUTE OF TECHNOLOGY ROORKEE   (India)

^b MICROSOFT RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CO-TRAINING; ENSEMBLE CLASSIFIERS; IMAGE SEARCH; INACCURATE INFORMATION; META-TAGS; SEMI-SUPERVISED;

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); CLASSIFIERS; COMPUTER SOFTWARE; COMPUTER VISION; FEATURE EXTRACTION; IMAGE PROCESSING; IMAGE RETRIEVAL; INFORMATION SERVICES; KETONES; PATTERN RECOGNITION; SEARCH ENGINES;

LEARNING SYSTEMS;

EID: 52049124229     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPRW.2008.4562952     Document Type: Conference Paper

References (18)

1. A. Blum and T. Mitchell. Combining labeled and unlabeled data with co-training. In Proc. of the Workshop on Computational Learning Theory, pages 92-100, 1998. 2
2. R. Fergus, L. Fei-Fei, P. Perona, and A. Zisserman. Learning object categories from google's image search. In Proc. the Tenth IEEE International. Conference on Computer Vision, pages 1816-1823, 2005.
3. Z. hua Zhou and M. Li. Tri-training: Exploiting unlabeled data using three classifiers. IEEE Trans. on Knowledge and Data Engineering, 17(11):1529-1541, November 2005. 2
4. F. Jurie and B. Triggs. Creating efficient codebooks for visual recognition. In Proc. the Tenth IEEE International Conference on Computer Vision, pages 604-610, 2005. 2
5. S. Lazebnik, C. Schmid, and J. Ponce. Affinc-invariant local descriptors and neighborhood statistics for texture recognition. In Proc. the Ninth IEEE International Conference on Computer Vision, pages 649-655, 2003. 2
6. A. Levin, P. Viola, and Y. Freund. Unsupervised improvement of visual detectors using co-training. In Proc. of the Ninth IEEE International Conference on-Computer Vision, pages 626-633, 2003. 2
7. D. G. Lowe. Distinctive image features from scaleinvarianl keypoinls. International Journal of Computer Vision, 60(2):91-110, 2004. 2
8. I. Matveeva, C. Burges, T. Burkard, A. Laucius, and L. Wong. High accuracy retrieval with multiple nested ranker. In Proc. the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pages 437-444, 2006. 1
9. K. Mikolajczyk and C. Schmid. A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell., 27(10):1615-1630, 2005. 2
10. F. Moosmann, B. Triggs, and F. Jurie. Fast discriminative visual codebooks using randomized clustering forests. In Proc. of Advances in Neural Information Processing Systems 19, pages 985-992, 1997. 2, 3
11. K. Nigam, A. K. McCallum, S. Thrun, and T. Mitchell. Text classification from labeled and unlabeled documents using cm. Machine Learning, 39(2-3):103-134, 2000. 2
12. E. Nowak, F. Jurie, and B. Triggs. Sampling strategies for bag-of-features image classification. In Proc. European Conference on Computer Vision, pages 490-503, 2006. 2
13. J. Sivic and A. Zisserman. Video google: A text retrieval approach to object matching in videos. In Proc. the Ninth IEEE International Conference on Computer Vision, pages 1470-1477, 2003. 2
14. A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain. Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mack Intell., 22(12):1349-1380, 2000. 1
15. P. E. Utgoff, N. C. Berkman, and J. A. Clouse. Decision tree induction based on efficient tree restructuring. Machine Learning, 29(1):5-44, 1997. 3
16. J. Winn, A. Criminisi, and T. Minka. Object categorization by learned universal visual dictionary. In Proc. the Tenth IEEE International Conference on Computer Vision, pages 1800-1807, 2005. 2
17. D. Zhou, J. Huang, and B. Schölkopf. Learning from labeled and unlabeled data on a. directed graph. In Proc. the 22nd international conference on Machine learning, pages 1036-1043, New York, NY, USA, 2005. 2
18. X. Zhu. Semi-supervised learning literature survey. Technical Report 1530, Computer Sciences, University of Wisconsin-Madison, 2005. 2