Information theoretic metrics in shot boundary detection

Authors:
Wengang Cheng;De Xu;Yiwei Jiang;Congyan Lang
Affiliations:
Department of Computer Science, Beijing Jiaotong Univ., Beijing, P.R. China;Department of Computer Science, Beijing Jiaotong Univ., Beijing, P.R. China;Department of Computer Science, Beijing Jiaotong Univ., Beijing, P.R. China;Department of Computer Science, Beijing Jiaotong Univ., Beijing, P.R. China
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Year:
2005

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Abstract

A favorable difference metric is crucial to the shot boundary detection (SBD) performance. In this paper, we propose a new set of metrics, information theoretic metrics, to quantitatively measure the changes between frames. It includes image entropy difference, joint entropy, conditional entropy, mutual information and divergence. They all can be used to cut detection. Specially, the image entropy and joint entropy are good clues to fade detection, while mutual information, joint entropy and conditional entropy are less sensitive to illumination variations. The theoretic analysis and experimental results show that they are useful in SBD.