Video abstraction generation supervised by user preference

Nhan Nguyen-Thanh, Truong Cong Thang, Beet Nara Bae, Yong Man Ro

Abstract: In this paper we propose an automated abstract extraction for soccer video using MPEG-7 descriptors. The video abstraction is created in form of highlight scenes which represent some pre-defined contexts. For soccer video, the events often have some specific order of actions, so the Hidden Markov Models (HMMs) are employed to detect the interested highlights. In our system, the input video is first separated into shots, then the video shots are classified and clustered based on the features of MPEG-7 standard descriptors. The HMM-based detection has two layers. The first is to eliminate the trivial scenes, and the second is to distinguish the highlights of different semantic events. Specific user preference will be used to select the highlight scenes relevant to the interest of user.