A Novel Framework for Semantic Annotation and Personalized Retrieval of Sports Video

Abstract

Sports video annotation is important for sports video semantic analysis such as event detection and personalization. In this paper, we propose a novel approach for sports video semantic annotation and personalized retrieval. Different from the state of the art sports video analysis methods which heavily rely on audio/visual features, the proposed approach incorporates web-casting text into sports video analysis. Compared with previous approaches, the contributions of our approach include the following. 1) The event detection accuracy is significantly improved due to the incorporation of web-casting text analysis. 2) The proposed approach is able to detect exact event boundary and extract event semantics that are very difficult or impossible to be handled by previous approaches. 3) The proposed method is able to create personalized summary from both general and specific point of view related to particular game, event, player or team according to user's preference. We present the framework of our approach and details of text analysis, video analysis, text/video alignment, and personalized retrieval. The experimental results on event boundary detection in sports video are encouraging and comparable to the manually selected events. The evaluation on personalized retrieval is effective in helping meet users' expectations.