A study of discriminant visual descriptors for sport video shot boundary detection

Video shot boundary detection is the process of automatically detecting the meaningful boundary content in video. Most shot boundary categorisation techniques use features extracted from the video frames to highlight the transition points between meaningful scenes. In this paper, a combination of global and local feature descriptors is proposed to represent the temporal characteristic in video. Motivated by the practical applications with moderate computational time, a video shot boundary detection scheme using supervised learning is proposed. The performance evaluation is constructed on a golf video dataset using the precision and recall performance measures.