A Joint Appearance-Spatial Distance for Kernel-Based Image Categorization

Abstract

The goal of image categorization is to classify a collection of unlabeled images into a set of predefined classes to support semantic-level image retrieval. The distance measures used in most existing approaches either ignored the spatial structures or used them in a separate step. As a result, these distance measures achieved only limited success. To address these difficulties, in this paper, we propose a new distance measure that integrates joint appearance-spatial image features. Such a distance measure is computed as an upper bound of an information-theoretic discrimination, and can be computed efficiently in a recursive formulation that scales well to image size. In addition, the upper bound approximation can be further tightened via adaption learning from a universal reference model. Extensive experiments on two widely-used data sets show that the proposed approach significantly outperforms the state-of-the-art approaches.