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31 July 2002 Incorporating negative examples in one-class SVM for relevance feedback in image retrieval
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Proceedings Volume 4875, Second International Conference on Image and Graphics; (2002) https://doi.org/10.1117/12.477119
Event: Second International Conference on Image and Graphics, 2002, Hefei, China
Abstract
Relevance feedback has been proven to be an effective scheme in content-based image retrieval to improve retrieval performance. Recently, SVM based techniques are introduced into the learning process of relevance feedback, for its good generalization ability in a high dimensional space in condition of small example size. Based on the extended one-class SVM that can handle negative examples, we propose a new relevance feedback scheme to overcome the limitation of the recent methods. The scheme is flexible to handle the situations with and without negative examples; and can further improve the retrieval performance when negative examples provided. The new scheme was evaluated on a database of 6,000 images and compared to previous methods. Experimental results have demonstrated its effectiveness.
© (2002) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hong Wu, Hanqing Lu, and Songde Ma "Incorporating negative examples in one-class SVM for relevance feedback in image retrieval", Proc. SPIE 4875, Second International Conference on Image and Graphics, (31 July 2002); https://doi.org/10.1117/12.477119
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