Improving the precision of CBIR systems by color and texture feature adaptation using GSA

Content-based image retrieval، CBIR، is an interesting problem of pattern recognition. This paper is devoted to the presentation an approach to reduce the semantic gap between low level visual features and high level semantics by parameter adaptation in feature extraction sub-block. In the proposed method، GSA is used. In texture feature extraction، the parameters of a 6-tap parametrized orthogonal mother wavelet and in color feature extraction، the quantization levels are adapted to reach maximum precision of the image retrieval system. Experimental results and comparison with the conventional CBIR system are reported on a database of 1000 images. Results confirm the efficiency of the proposed adapted image retrieval system.