Active concept learning for image retrieval in dynamic databases

Proceedings of the IEEE International Conference on Computer Vision

Volumn 1, Issue , 2003, Pages 90-95

  Dong, Anlei ^a   Bhanu, Bir ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATABASE SYSTEMS; LEARNING SYSTEMS; MATHEMATICAL MODELS; PARAMETER ESTIMATION;

CONCEPT LEARNING; DATABASE IMAGE DISTRIBUTION; MODEL SELECTION;

CONTENT BASED RETRIEVAL;

EID: 0345414150     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/iccv.2003.1238318     Document Type: Conference Paper

References (16)

1. J. J. Rocchio and G. Salton. "Information search optimization and iterative retrieval techniques," Proc. AFIPS, vol. 27, pp. 293-305, 1965.
2. Y. Rui, T.S. Huang, M. Ortega, and S. Mehrotra, "Relevance feedback: a power tool for interactive content-based image retrieval," IEEE Trans. on Circuits and Systems for Video Technology, vol. 8 (5), pp. 644-655, Sept. 1998.
3. J. Peng, B. Bhanu, and S. Qing, "Probabilistic feature relevance learning for content-based image retrieval," CVIU, vol. 75, no. 1-2, pp. 150-164, July-August 1999.
4. I. J. Cox, M. L. Matt, T. P. Minka, T. V. Papathoma, and P. N. Yianilos, "The Bayesian image retrieval system, PicHunter: theory, implementation, and psychophysical experiments," IEEE Trans. on Image Processing, vol. 9 (1), pp. 20-37, Jan. 2000.
5. P. Yin, B. Bhanu, K. Chang, and A. Dong, "Improving retrieval performance by long-term relevance information," Proc. ICPR, vol. III, pp. 533-536, Aug. 2002.
6. B. Bhanu and A. Dong, "Concept learning with fuzzy clustering and relevance feedback," Engineering Applications of Artificial Intelligence, vol. 15, pp. 123-138, April 2002.
7. N. Vasconcelos, Bayesian Models for Visual Information Retrieval, Ph.D. thesis, MIT, 2000.
8. M. Figueiredo and A. K. Jain, "Unsupervised learning of finite mixture models," IEEE Trans. PAMI, vol. 24 (3), pp. 381-396, March 2002.
9. G. Schwarz, "Estimating the dimension of a model," Annals of Statistics, vol. 6, pp. 461-464, 1978.
10. M. Whindham and A. Cutler, "Information ratios for validating mixture analysis," Journal of Royal Statistical Soc., vol. 87, pp. 1188-1192, 1992.
11. Y. Wu and T. S. Huang, "Towards self-exploring discriminating features for visual learning," Engineering Applications of Artificial Intelligence, vol. 15, pp. 139-150, Sept. 2002.
12. K. Nigam, A. McCallum, S. Thrun, and T. Mitchell, "Text classification from labeled and unlabeled documents using EM," Machine Learning, vol. 39, pp. 103-134, 1999.
13. A. Dong and B. Bhanu, "A new semi-supervised EM algorithm for image retrieval," Proc. CVPR, vol. II, pp. 662-667, 2003.
14. A. Dong and B. Bhanu, "Concept learning and transplantation for dynamic image databases," Proc. ICME, vol. I, pp. 765-768, 2003.
15. G. McLachlan and D. Peel, Finite Mixture Models, John Wiley & Sons, 2000.
16. R. J. Hathaway, "Another interpretation of the EM algorithm for mixture distributions," Statistics & Probability Letters, vol. 4, pp. 53-56, 1986.