personalized retrieval of sports video
There has been a growing demand for effective access to video information from media archives in recent years. Personalized video retrieval is one of the most challenging issues and has spurred a significant interest in many research communities. In this paper, a novel approach is proposed to achieve personalized retrieval of sports video, which includes two research tasks: semantic annotation of sports video and acquisition of user's preference. For semantic annotation, a multi-modal framework is employed to detect sports event and index the sports video content. Web-casting text, as external information, is utilized to detect semantic events in sport videos. The semantic concepts and keywords included in the web-casting text are extracted to annotate and index the sport event segments automatically. For user's preference acquisition, relevance feedback is applied to model user's preference and non-preference, and re-ranking is used to refine the results. First, the user is asked to label some video segments as desirable and undesirable. Then, we use these labels to infer the user's interesting points (e.g. the player, the event type, the team, etc.) by analysis of text keywords; the low-level video features are also adopted as a supplementary to reflect the user's preference. The overall new rank of the results is the combination of the user's high-level and low-level preference. Experiments conducted on real-world soccer game videos show that the proposed method has an encouraging performance.
video retrieval semantic annotation video indexing