Sequential association mining for video summarization

Authors:
Xingquan Zhu;Xindong Wu
Affiliations:
Dept. of Comput. Sci., Vermont Univ., Burlington, VT, USA;Dept. of Comput. Sci., Vermont Univ., Burlington, VT, USA
Venue:
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Year:
2003

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Abstract

In this paper, we propose an association-based video summarization scheme that mines sequential associations from video data for summary creation. Given detected shots of video V, we first cluster them into visually distinct groups, and then construct a sequential sequence by integrating the temporal order and cluster type of each shot. An association mining scheme is designed to mine sequentially associated clusters from the sequence, and these clusters are selected as summary candidates. With a user specified summary length, our system generates the corresponding summary by selecting representative frames from candidate clusters and assembling them by their original temporal order. The experimental evaluation demonstrates the effectiveness of our summarization method.