An Intelligence Image Retrieval System Based On Evolutionary Algorithm

Abstract

 Digital image libraries and other multimedia databases have been dramatically expanded in recent years. In order to effectively and precisely retrieve the desired images from a large image database, the development of a content-based image retrieval (CBIR) system has been used. However, most of the proposed approaches emphasize on finding the best representation for different image features. In this paper, a user-interactive mechanism for CBIR method based on Particle Swarm Optimization (PSO) is proposed. Color attributes like the mean value, the standard deviation, and the image bitmap of a color image are used as the features for retrieval. In addition, the entropy based on the gray level co-occurrence matrix and the edge histogram of an image is also considered as the texture features. Furthermore, to reduce the gap between the retrieval results and the users’ expectation, the PSO is employed to help the users identify the images that are most satisfied to the users’ need .Experimental results and comparisons demonstrate the feasibility of the proposed approach.