A Novel Approach for Video Classification Based on Association Rules

Abstract

Video classification is an important task for processing, analysis and retrieval of videos. The traditional method of Video classification generally use HMM theoretic. However, the HMM method has limitation to analysis video data. To solve this problem, we proposed a novel approach for video classification which uses the association rules. Firstly, we mined the actual dependence relationship between video states when the state model is constructed. Secondly, the reliability of dependence relationship is predicted with the restriction of association distance. Furthermore, the association rules are obtained by exploring state transition patterns. The experiment results demonstrate that the proposed method is efficient and suitable for various types of video data.