Reinforcement learning for combining relevance feedback techniques

Proceedings of the IEEE International Conference on Computer Vision

Volumn 1, Issue , 2003, Pages 510-515

  Yin, Peng Yeng ^a   Bhanu, Bir ^b   Chang, Kuang Cheng ^a   Dong, Anlei ^b  

^a NATIONAL CHI NAN UNIVERSITY   (Taiwan)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA STORAGE EQUIPMENT; DATABASE SYSTEMS; PROBABILITY DISTRIBUTIONS; QUERY LANGUAGES;

FEATURE RELEVANCE ESTIMATION; QUERY VECTOR MODIFICATION; REINFORCEMENT LEARNING; RELEVANCE FEEDBACK;

INFORMATION RETRIEVAL;

EID: 0345414070     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (6)

1. J. J. Rocchio, Jr., Relevance feedback in information retrieval, in The Smart System, Prentice Hall, NJ (1971) 313-323.
2. J. Peng, B. Bhanu, and S. Qing, Probabilistic feature relevance learning for content-based image retrieval, Computer Vision and Image Understanding 75 No. 1-2 (1999) 150-164.
3. I. Cox, M. Miller, T. Minka, T. Papathomas and P. Yianilos, The Bayesian image retrieval system, PicHunter: theory, implementation, and psychophysical experiments, IEEE Trans. On Image Processing 9 (2000) 20-37.
4. Y. Rui, T. S. Huang, M. Ortega and S. Mehrotra, Relevance feedback: a power tool for interactive content-based image retrieval, IEEE Trans. On Circuit System for Video Technology 8 (1998) 644-655.
5. C. Watkins and P. Dayan, Q-learning, Machine Learning 8 (1992) 279-292.
6. Vision and Intelligent Systems Lab (VISLab), University of California, Riverside. http://www.vislab.ucr.edu.