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 Ws-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. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for...

• Type: Conference
• Year: 2004
• Where: SPIESR
• Authors: King-Shy Goh, Koji Miyahara, Regunathan Radhakrishnan, Ziyou Xiong, Ajay Divakaran
• Keywords: Database, Electric Research Laboratories, Mitsubishi Electric Research, SPIESR 2004, Television Programs