Design of a two-stage content-based image retrieval system using texture similarity

Abstract

Efficacy and efficiency are two important issues in designing content-based image retrieval systems. In this paper, we present an efficient two-stage image retrieval system with high performance of efficacy based on two novel texture features, the composite sub-band gradient (CSG) vector and the energy distribution pattern (EDP)-string. Both features are generated from the sub-images of a wavelet decomposition of the original image. At the first stage, a fuzzy matching process based on EDP-strings is performed and serves as a signature filter to quickly remove a large number of non-promising database images from further consideration. At the second stage, the images passing through the filter will be compared with the query image based on their CSG vectors for detailed feature inspection. By exercising a database of 2400 images obtained from the Brodatz album, we demonstrated that both high efficacy and high efficiency can be achieved simultaneously by our proposed system.