A new analysis of the value of unlabeled data in semi-supervised learning for image retrieval

2004 IEEE International Conference on Multimedia and Expo (ICME)

Volumn 2, Issue , 2004, Pages 1019-1022

  Tian, Qi ^a   Yu, Jie ^a   Xue, Qing ^a   Sebe, Nicu ^b  

^a Science Building   (United States)

^b UNIVERSITY OF AMSTERDAM   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

CONTENT-BASED IMAGE RETRIEVAL (CBIR); DATA DISTRIBUTION; SEMI-SUPERVISED LEARNING; UNLABELED DATA;

COMPUTER VISION; DATA REDUCTION; FEEDBACK; IMAGE RETRIEVAL; MAXIMUM LIKELIHOOD ESTIMATION; WORLD WIDE WEB;

LEARNING SYSTEMS;

EID: 11244302729     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. A. Blum, and T. Mitchell, "Combining labeled and unlabeled data with co-training," Annual Conf. Comput. Learning Theory, pp. 92-100, New York: ACM, 1998.
2. K. Nigam, A McCallum, S. Thrun, and T. Mitchell, 'Text classification from labeled and unlabeled documents using EM," Machine Learning, 39, pp. 1-32, 2000.
3. B. M. Shahshahani, and D. A. Landgrebe, "The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon," IEEE Trans. Geoscience and Remote Sensing, vol. 32, no. 5, pp. 1087-1095, 1994.
4. I. Cohen, N. Sebe, F. G. Cozman, M. C. Cirelo, and T. S. Huang, "Semi-supervised learning of classifiers: theory and algorithms for Bayesian network classifiers and applications to human-computer interaction," submitted to IEEE Trans. on PAMI, in revision, 2003.
5. D. Cohn, Z. Ghahramani, and M. Jordan, "Active learning with mixture models," Multiple Model Approaches to Modeling and Control, R. Murray-Smith, T. Johanson (Eds.), pp. 167-183, 1997.
6. Y. Freund, H. Seung, E. Shamir, and N. Tishby, "Selective sampling using the query by committee algorithm," Machine Learning, 28, pp. 133-168, 1997.
7. S. Tong, and E. Chang, "Support vector machine active learning for image retrieval," Proc. of ACM Int'l. Conf. Multimedia, pp. 107-118, 2001.
8. I. J. Cox, et al. "The Bayesian image retrieval system, PicHunter: theory, implementation, and psychophysical experiments," IEEE Trans. Image Proc., 9(1):20-37, 2000.
9. F. G. Cozman, and I. Cohen, "Unlabeled data can degrade classification performance of generative classifiers," Int'l Florida Artificial Intell. Society Conf., pp. 327-331, 2002.
10. R. Duda, P. Hart, and D. Stork, Pattern Classification, second edition, John Wiley & Sons, Inc., 2001.
11. H. Stark, J. Woods, Probability, Random Process and Estimation Theory for Engineers, Prentice Hall, 1994.
12. H. Motulsky, Intuitive biostatistics: choosing a statistical test, Oxford Univ. Press Inc., 1995.
13. Q. Tian, Content-based image visualization and retrieval for image libraries, Ph.D. dissertation, University of Illinois at Urbana-Champaign, IL, U.S.A., 2002.