Large-Scale Image Search using Region Division

Abstract

In this paper, we focus on the problem of image feature extraction and similarity measure using region division search. Specifically, we proposed a novel image region division to roughly mimic the location distribution of image color and deal with the color histogram failing to describe spatial information. Furthermore, an image descriptor combining local color histogram and Gabor texture features with reduced feature dimensions are developed for optimizing our region division search method. Moreover, an extended Canberra distance is proposed for images similarity measure to increase the faulttolerant ability of the whole large-scale image search. Extensive experiments on several benchmark image retrieval databases validate the superiority of the proposed approaches.