Adaptive histograms and dissimilarity measure for texture retrieval and classification

Abstract

Histogram-based dissimilarity measures are extensively used for content-based image retrieval. In an earlier paper [1], we proposed an efficient weighted correlation dissimilarity measure for adaptive-binning color histograms. Compared to existing fixed-binning histograms and dissimilarity measures, adaptive histograms together with weighted correlation produce the best overall performance in terms of high accuracy, small number of bins, no empty bin, and efficient computation for image classification and retrieval. This paper follows up on the study of adaptive histograms by applying them to texture classification, retrieval, and clustering. Adaptive histograms are generated from the amplitude of the discrete Fourier transform of images. Extensive comparisons with well-known texture features and dissimilarity measures show that, again, adaptive histograms and weighted correlation produce good overall performance.