Interactive differential evolution for user-oriented image retrieval system

摘要

Large amounts of image data have been produced on the Internet over the past several years. As a kind of effective retrieval way, the content-based image retrieval (CBIR) has attracted more and more attention. To improve the preciseness, most CBIR systems emphasize on finding the best representation for different image features. However, the semantic gap between visual description and user expectations is hard to handle. The relevance feedback technique can use relevance information to alleviate this problem. This paper describes a CBIR framework based on interactive differential evolution which uses a technique of combing the global and the local retrieval strategy to help users retrieve their preferred images in a user-oriented way. Experimental results show that the proposed framework increases the accuracy, and it outperforms the recent frameworks based on GAs.