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18 December 2003 Audio-visual event detection based on mining of semantic audio-visual labels
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Removing commercials from television programs is a much sought-after feature for a personal video recorder. In this paper, we employ an unsupervised clustering scheme (CM_Detect) to detect commercials in television programs. Each program is first divided into W8-minute chunks, and we extract audio and visual features from each of these chunks. Next, we apply k-means clustering to assign each chunk with a commercial/program label. In contrast to other methods, we do not make any assumptions regarding the program content. Thus, our method is highly content-adaptive and computationally inexpensive. Through empirical studies on various content, including American news, Japanese news, and sports programs, we demonstrate that our method is able to filter out most of the commercials without falsely removing the regular program.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
King-Shy Goh, Koji Miyahara, Regunathan Radhakrishnan, Ziyou Xiong, and Ajay Divakaran "Audio-visual event detection based on mining of semantic audio-visual labels", Proc. SPIE 5307, Storage and Retrieval Methods and Applications for Multimedia 2004, (18 December 2003);


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