Bridging the semantic gap in sports

One of the major challenges facing current media management systems and related applications is the so-called ‘‘semantic gap’’ between the rich meaning that a user desires and the shallowness of the content descriptions that are automatically extracted from the media. In this paper, we address the problem of bridging this gap in the sports domain. We propose a general framework for indexing and summarizing sports broadcast programs, with a high-level model of sports broadcast video using the concept of an event, defined according to domainspecific knowledge for different types of sports. Within this general framework, we develop automatic event detection algorithms that are based on automatic analysis of the visual and aural signals in the media. We have successfully applied the event detection algorithms to different types of sports including American football, baseball, Japanese sumo wrestling, and soccer. Event modeling and detection contribute to the reduction of the se...

• Type: Conference
• Year: 2003
• Where: SPIESR
• Authors: Baoxin Li, James H. Errico, Hao Pan, M. Ibrahim Sezan
• Keywords: Database, Event Detection Algorithms, SPIESR 2003, Sports, Sports Broadcast