Shot partitioning based recognition of TV commercials

Digital video applications exploit the intrinsic structure of video sequences. In order to obtain and represent this structure for video annotation and indexing tasks, the main initial step is automatic shot partitioning. This paper analyzes the problem of automatic TV commercials recognition, and a new algorithm for scene break detection is then introduced. The structure of each commercial is represented by the set of its key-frames, which are automatically extracted from the video stream. The particular characteristics of commercials make commonly used shot boundary detection techniques obtain worse results than with other video content domains. These techniques are based on individual image features or visual cues, which show significant performance lacks when they are applied to complex video content domains like commercials. We present a new scene break detection algorithm based on the combined analysis of edge and color features. Local motion estimation is applied to each edge in a frame, and the continuity of the color around them is then checked in the following frame. By separately considering both sides of each edge, we rely on the continuous presence of the objects and/or the background of the scene during each shot. Experimental results show that this approach outperforms single feature algorithms in terms of precision and recall.