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2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. (2003)
Madison, Wisconsin
June 18, 2003 to June 20, 2003
ISSN: 1063-6919
ISBN: 0-7695-1900-8
pp: 649
Yimin Wu , SUNY at Buffalo
Aidong Zhang , SUNY at Buffalo
ABSTRACT
Relevance feedback has been an indispensable component for multimedia retrieval systems. In this paper, we present an adaptive pattern discovery method, which addresses relevance feedback by interactively discovering meaningful patterns of relevant objects. To facilitate pattern discovery, we first present a dynamic feature extraction method, which aims to alleviate the curse of dimensionality by extracting a feature subspace using balanced information gain. In the feature subspace, we train an online pattern classification method called adaptive random forests to classify multimedia objects as relevant or irrelevant. Our adaptive random forests adapts the traditional classification method known as random forests for relevance feedback. It improves the efficiency of pattern discovery by choosing the most-informative samples for online learning. Extensive experiments are carried out on a Corel image set (with 31,438 images) to evaluate the performance of our method as compared against the state-of-the-art approaches.
INDEX TERMS
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CITATION

Y. Wu and A. Zhang, "Adaptive Pattern Discovery for Interactive Multimedia Retrieval," 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings.(CVPR), Madison, Wisconsin, 2003, pp. 649.
doi:10.1109/CVPR.2003.1211528
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