Relevance feedback is a powerful tool to grasp the user's intention in image retrieval systems and has attracted many researchers' attention since the 1990s. A feature filter, whose parameters are computed by a statistical resampling approach, is proposed in order to select the unique features to characterize the positive samples. A statistical voting procedure is then adopted to rank the candidates after getting rid of irrelevant feature components. Experimental results show that the proposed approach is more efficient and robust than the traditional method. Copyright © 2004 by the Institute of Electrical and Electronics Engineers, Inc.
|Title of host publication||Proceedings of 2004 International Symposium on Intelligent Multimedia, Video and Speech processing, ISIMP 2004|
|Place of Publication||Piscataway, NJ|
|Publication status||Published - 2004|