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Proceedings Paper

Scene-based scalable video summarization
Author(s): Ying Li; C. C. Jay Kuo; Daniel Tretter
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Paper Abstract

A scalable video summarization and navigation system is proposed in this work. Particularly, given the desired number of keyframes for a video sequence, we first distribute it among underlying video scenes and sinks based on their respective importance ranks. Then, we select the most important shot of each sink as its R-shot and further assign each sink's designated number of keyframes to its R-shot. Finally, a time-constrained keyframe extraction scheme is developed to locate all keyframes. Consequently, we can achieve a scalable video summary from the initial keyframe set by exploiting such a video structure-based ranking scheme. In addition, a content navigation tool is also developed which could help users freely access or locate specific video scenes or shots. Sophisticated user studies have shown that this summarization and navigation system can not only help users quickly browse video content, but also assist them in searching for particular video segments.

Paper Details

Date Published: 26 November 2003
PDF: 12 pages
Proc. SPIE 5242, Internet Multimedia Management Systems IV, (26 November 2003); doi: 10.1117/12.511559
Show Author Affiliations
Ying Li, IBM Thomas J. Watson Research Ctr. (United States)
C. C. Jay Kuo, Univ. of Southern California (United States)
Daniel Tretter, Hewlett-Packard Labs. (United States)


Published in SPIE Proceedings Vol. 5242:
Internet Multimedia Management Systems IV
John R. Smith; Sethuraman Panchanathan; Tong Zhang, Editor(s)

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