Histogram-Based Search: A Comparative Study

Abstract

Histograms represent a popular means for feature representation. This paper is concerned with the problem of exhaustive histogram-based image search. Several standard histogram construction methods are explored, including the conventional approach, Huangpsilas method, and the state-of-the-art integral histogram. In addition, we present a novel multiscale histogram-based search algorithm, termed the distributive histogram, that can be evaluated exhaustively in a fast and memory efficient manner. An extensive systematic empirical evaluation is presented that explores the computational and storage consequences of altering the search image and histogram bin sizes. Experiments reveal up to an eight-fold decrease in computation time and hundreds- to thousands-fold decrease of memory use of the proposed distributive histogram in comparison to the integral histogram. Finally, we conclude with a discussion on the relative merits between the various approaches considered in the paper.