Content-based similarity retrieval of images based on spatial color distributions

Abstract

The importance of image retrieval systems which search image databases for images that users want to see has been increasing, while advances in computer technology have enabled us to make large image databases easily. This paper proposes a content-based image retrieval (CBIR) system in which an example image is given as a query. One of the most important factors of CBIR using a pictorial query is the design of features which represent the appearance of the query image. To capture the color distribution of images, the proposed method uses multiple local color histograms corresponding to segmented sub-regions, while most previous histogram-based image retrieval systems have used a global single color histogram for an entire image. Segmentation of an image is done by dividing the image into two rectangular regions recursively based on the discriminant analysis. The resulting binary tree structure of regions facilitates the evaluation of similarity among images. The paper also describes two kinds of experiments and results to demonstrate the discriminant ability of our system and to evaluate the consistency of the proposed similarity function and a human sense of similarity of images. Results show that our system can effectively capture spatial color information and retrieve similar appearance images in comparison with the single histogram method. It was also confirmed that the proposed method improves the consistency of the similarity function and a human sense for many query images in the experiment.