Audio-visual event detection based on mining of semantic audio-visual labels

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 W-s-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.