Video shot grouping using best-first model merging

For more efficiently organizing, browsing, and retrieving digital video, it is important to extract video structure information at both scene and shot levels. This paper present an effective approach to video scene segmentation based on probabilistic model merging. In our proposed method, we regard the shots in video sequence as hidden state variable and use probabilistic clustering to get the best clustering performance. The experimental results show that our method produces reasonable clustering results based on the visual content. A project named HomeVideo is introduced to show the application of the proposed method for personal video materials management.