Information-Theoretic Content Selection for Automated Home Video Editing

In automated home video editing, selecting out the most informative contents from the redundant footage is challenging. This paper proposes an information-theoretic approach to content selection by exploring the dependence relations between who (characters) and where (scenes) in the video. First the footage is segmented into basic units about the same characters at the same scene. To compactly represent the dependence relations between scenes and characters, contingency table is used to model their co-occurrence statistics. Suppose the contents about which characters at which scene are dominating by two random variables, an optimal selection criterion is proposed based on joint entropy. To improve the computation efficiency, a pruned N-Best heuristic algorithm is presented to search the most informative video units. Experimental results demonstrated the proposed approach is flexible and effective for automated content selection.