Video summarization by redundancy removing and content ranking

Authors:
Tao Wang;Yue Gao;Patricia P. Wang;Eric Li;Wei Hu;Yimin Zhang;Junhai Yong
Affiliations:
Intel China Research Center, Beijing, China;Tsinghua University, Beijing, China;Intel China Research Center, Beijing, China;Intel China Research Center, Beijing, China;Intel China Research Center, Beijing, China;Intel China Research Center, Beijing, China;Tsinghua University, Beijing, China
Venue:
Proceedings of the 15th international conference on Multimedia
Year:
2007

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Abstract

In order to help the user to grasp the long video content quickly, this paper proposes a novel video summarization approach based on redundancy removal and content ranking. By video parsing and cast indexing, the approach first constructs a story board to let user know about the main scenes and the main actors in the video. Then it generates a "story-constraint summary" by key frame clustering and repetitive segment detection. To shorten the video summary length to a target length, our approach constructs a "time-constraint summary" by important factor based content ranking. Extensive experiments are carried out on TV series, movies, and cartoons. Good results demonstrate the effectiveness of the proposed method.