Story based representation for broadcasted sports video and automatic story segmentation
This paper presents a model to represent a broadcasted sports video in a semantical way and proposes a method to segment the sports video into the semantical units for the representation. Representation of a video should clarify its semantical content as accurately as possible. Our model structurizes the video and gives suitable semantical descriptions to its particular time locations based on the structures of the sports video. We consider the speech transcript as the useful information stream for the generation of the semantical descriptions. The proposed method tries to segment the speech transcript into the units of the structurization in a probabilistic framework based on Bayesian networks. Moreover, the association with the image stream enables us to find the corresponding video segments. We discuss some experimental results.