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Proceedings Paper

Discriminative genre-independent audio-visual scene change detection
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Paper Abstract

We present a technique for genre-independent scene-change detection using audio and video features in a discriminative support vector machine (SVM) framework. This work builds on our previous work by adding a video feature based on the MPEG-7 "scalable color" descriptor. Adding this feature improves our detection rate over all genres by 5% to 15% for a fixed false positive rate of 10%. We also find that the genres that benefit the most are those with which the previous audio-only was least effective.

Paper Details

Date Published: 19 January 2009
PDF: 8 pages
Proc. SPIE 7255, Multimedia Content Access: Algorithms and Systems III, 725502 (19 January 2009); doi: 10.1117/12.805624
Show Author Affiliations
Kevin W. Wilson, Mitsubishi Electric Research Lab. (United States)
Ajay Divakaran, Sarnoff Corp. (United States)


Published in SPIE Proceedings Vol. 7255:
Multimedia Content Access: Algorithms and Systems III
Raimondo Schettini; Ramesh C. Jain; Simone Santini, Editor(s)

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