Highlight detection is a form of video summarization techniques aiming at including the most expressive or attracting parts in the video. Most video highlights selection research work has been performed on sports video, detecting certain objects or events such as goals in soccer video, touch down in football and others. In this paper, we present a highlight detection method for film video. Highlight section in a film video is not like that in sports video that usually has certain objects or events. The methods to determine a highlight part in a film video can exhibit as three aspects: (a) locating obvious audio event, (b) detecting expressive visual content around the obvious audio location, (c) selecting the preferred portion of the extracted audio-visual highlight segments. We define a double filters model to detect the potential highlights in video. First obvious audio location is determined through filtering the obvious audio features, and then we perform the potential visual salience detection around the potential audio highlight location. Finally the production from the audio-visual double filters is compared with a preference threshold to determine the final highlights. The user study results indicate that the double filters detection approach is an effective method for highlight detection for video content analysis.
Video segmentation, storage and content-based retrieval become essential with the development of government affairs through network and web search. Segmentation and clustering of the video sequences are the first step should be taken to solve the problem. The conventional segmentation methods are based on histogram difference and pixel difference between the successive frame pairs, which usually can not show satisfying results. In this paper we make use of the mutual complementary features between the pixel gray distribution based method and the object outline based method throughout the frames to segment the video sequence hierarchically.