Improvement of Commercial Boundary Detection Using Audiovisual Features | Zendy

Jun-Cheng Chen | Zendy; Jen-Hao Yeh | Zendy; Wei-Ta Chu | Zendy; Jin-Hau Kuo | Zendy; JaLing Wu | Zendy

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Improvement of Commercial Boundary Detection Using Audiovisual Features

Author(s) -

Jun-Cheng Chen,

Jen-Hao Yeh,

Wei-Ta Chu,

Jin-Hau Kuo,

JaLing Wu

Publication year - 2005

Publication title -

lecture notes in computer science

Language(s) - English

Resource type - Book series

SCImago Journal Rank - 0.249

H-Index - 400

eISSN - 1611-3349

pISSN - 0302-9743

ISBN - 3-540-30027-9

DOI - 10.1007/11581772_68

Subject(s) - computer science , scheme (mathematics) , modal , construct (python library) , domain (mathematical analysis) , boundary (topology) , speech recognition , voice activity detection , artificial intelligence , speech processing , mathematics , mathematical analysis , chemistry , polymer chemistry , programming language

Detection of commercials in TV videos is difficult because the diversity of them puts up a high barrier to construct an appropriate model. In this work, we try to deal with this problem through a top-down approach. We take account of the domain knowledge of commercial production and extract features that describe the characteristics of commercials. According to the clues from speech-music discrimination, video scene detection, and caption detection, a multi-modal commercial detection scheme is proposed. Experimental results show good performance of the proposed scheme on detecting commercials in news and talk show programs.

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