A novel visualword assignment model for content-based image retrieval

Abstract

Visual bag of words model have been applied in the recent past for the purpose of content-based image retrieval. In this paper, we propose a novel assignment model of visual words for representing an image patch. In particular, a vector is used to represent an image patch with its elements denoting the affinities of the patch to belong to a set of closest/most influential visual words. We also introduce a dissimilarity measure, consisting of two terms, for comparing a pair of image patches. The first term captures the difference in affinities of the patches to belong to the common set of influential visual words. The second term checks the number of visual words which influences only one of the two patches and penalizes the measure accordingly. Experimental results on the publicly available COIL-100 image database clearly demonstrates the superior performance of the proposed content-based image retrieval (CBIR) method over some similar existing approaches.