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Published Articles >> Table of Contents >> Abstract
Ninth IEEE International Conference on Computer Vision (ICCV'03) - Volume 1
p. 90
Active Concept Learning for Image Retrieval in Dynamic Databases
Anlei Dong, University of California, Riverside
Bir Bhanu, University of California, Riverside
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICCV.2003.1238318
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| Abstract |
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Concept learning in content-based image retrieval (CBIR) systems is a challenging task. This paper presents an active concept learning approach based on mixture model to deal with the two basic aspects of a database system: changing (image insertion or removal) nature of a database and user queries. To achieve concept learning, we develop a novel model selection method based on Bayesian analysis that evaluates the consistency of hypothesized models with the available information. The analysis of exploitation vs. exploration in the search space helps to find optimal model efficiently. Experimental results on Corel database show the efficacy of our approach.
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 Additional Information
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Citation:
Anlei Dong, Bir Bhanu,
"Active Concept Learning for Image Retrieval in Dynamic Databases,"
iccv,
p. 90,
Ninth IEEE International Conference on Computer Vision (ICCV'03) - Volume 1,
2003
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