Integrating Semantic Video Understanding and Knowledge Visualization for Large-Scale News Video Exploration
Hangzai Luo, Jianping Fan, Jing Yang, William Ribarsky · 2006
In this paper, we have developed a novel framework to enable more effective visual analysis and exploration of large-scale news videos via knowledge visualization. A novel interestingness measurement for video news reports is proposed to enable analysts and general audiences to find news stories of interest at first glance and catch the valuable knowledge in large-scale video news databases. Keyframes, keywords and their relations are automatically extracted from news video clips and visually represented according to their interestingness measurement. Our techniques for intelligent news video analysis have the capacity to enable more effective visualization and exploration of large-scale news videos. Our visualization-based news video analysis and exploration system is very useful for analysts and general audiences to quickly find the news stories of interest from large-scale news videos extracted from many channels.