Automatic Segmentation of News Items Based on Video and Audio Features

The automatic segmentation of news items is a key for implementing the automatic cataloging system of news video. This paper presents an approach which manages audio and video feature information to automatically segment news items. The integration of audio and visual analyses can overcome the weakness of the approach using only image analysis techniques. It makes the approach more adaptable to various situations of news items. The proposed approach detects silence segments in accompanying audio, and integrates them with shot segmentation results, as well as anchor shot detection results, to determine the boundaries among news items. Experimental results show that the integration of audio and video features is an effective approach to solving the problem of automatic segmentation of news items. This work is supported by the National Natural Science Foundation of China (No. 69789301), National “863” High-Tech Programme of China (No.863-306-ZD03-01-2), and the 100 Outstanding Scientists Foundation of the Chinese Academy of Sciences. WANG Weiqiang was born in 1972. He is a Ph.D candidate at the Institute of Computing Technology, the Chinese Academy of Sciences. He received the B.S. degree and M.S. degree in computer science from the Harbin Engineering University, in 1995 and 1998 respectively. His current research interests include multimedia techniques and artificial intelligence. GAO Wen was born in 1956. He is a professor and doctoral supervisor of the the Institute of Computing Technology, the Chinese Academy of Sciences. His current research areas include multimedia, data compression, image processing, computer vision, multimodal interface, artificial intelligence, and virtual reality.