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Proceedings Paper

Detecting commercial breaks in real TV programs based on audiovisual information
Author(s): Ying Li; C.-C. Jay Kuo
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Paper Abstract

Detecting and extracting commercial breaks from a TV program is important for achieving efficient video storage and transmission. In this work , we approach this problem by utilizing both visual and audio information. Commercial breaks have several special characteristics such as a restricted temporal length, a high cut frequency, a high level of actions, delimiting black frames and silences, etc, which can be used for their separation from regular TV programs. A feature-based commercial break detection system is thus proposed to fulfill this task. We first perform a coarse-level detection of commercial breaks with pure visual information, since the high activity and the high cut frequency will somehow manifest themselves in the statistics of some measurable features. At the second step, we proceed to refine detected break boundaries by integrating audio clues. That is, there is always a short period of silence between commercial breaks and the TV program. Two audio features, i.e. the short- time energy and short-time average zero-crossing rate, are extracted for the silence detection purpose. At the last step, we return to the visual information domain again to achieve a frame-wise precision by locating the black frames. Extensive experiments show that by combining both visual and audio information, we can obtain accurate commercial break results.

Paper Details

Date Published: 11 October 2000
PDF: 12 pages
Proc. SPIE 4210, Internet Multimedia Management Systems, (11 October 2000); doi: 10.1117/12.403805
Show Author Affiliations
Ying Li, Univ. of Southern California (United States)
C.-C. Jay Kuo, Univ. of Southern California (United States)

Published in SPIE Proceedings Vol. 4210:
Internet Multimedia Management Systems
John R. Smith; Chinh Le; Sethuraman Panchanathan; C.-C. Jay Kuo, Editor(s)

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