Automatic story segmentation of news video based on audio-visual features and text information

In this paper a novel news story automatic segmentation scheme based on audio-visual features and text information is presented. The basic idea is to detect the shot boundaries for news video first, and then the topic-caption frames are identified to get segmentation cues by using text detection algorithm. In the next step, silence clips are detected by using short-time energy and short-time average zero-crossing rate (ZCR) parameters. At last, audio-visual features and text information are integrated to realize automatic story segmentation. On test data with 135, 400 frames, the accuracy rate 85.8% and the recall rate 97.5% are obtained. The experimental results show the approach is valid and robust.