Adaptive binning and dissimilarity measure for image retrieval and classification

Abstract

Color histogram is an important part of content-based image retrieval systems. It is a common understanding that histograms that adapt to images can represent their color distributions more efficiently than histograms with fixed binnings. However, among existing dissimilarity measures, only the Earth Mover's Distance can compare histograms with different binnings. This paper presents a detailed quantitative study of fixed and adaptive binnings and the corresponding dissimilarity measures. An efficient dissimilarity measure is proposed for comparing histograms with different binnings. Extensive test results show that adaptive binning and dissimilarity produce the best overall performance, in terms of good accuracy, small number of bins, no empty bin, and efficient computation, compared to existing fixed binning schemes and dissimilarity measures.