Measuring conceptual relation of visualwords for visual categorization

Abstract

Representing image using the distribution of local features on a group of visual words is an effective method for visual categorization. Visual words can be related conceptually and the information can be incorporated to enhance the performance. However, conventional methods usually use visual words independently without considering this. This paper proposes a novel approach to measure the conceptual relation of visual words and incorporate the information into visual categorization. The conceptual relation is measured by the similarity of class distributions induced by visual words, accordingly visual words are grouped and images are represented on multiple levels. Categorization is taken using the support vector machine (SVM) with an effective kernel designed for matching multi-level representations. The proposed method is evaluated for video events categorization on the benchmark dataset and shows superior performance to conventional methods.