Dynamic Summarization of Video Using Minimum Edge Weight Matching in Bipartite Graphs

To select the long-running videos from online archives and other collections, the users would like to browse, or skim through quickly to get a hint on the semantic content of the videos. Video summarization addresses this problem by providing a short video summary of a full-length video. An ideal video summary would include all the important segments of the video and remain short in length. The problem of summarization is extremely challenging and has been a widely pursued subject of recent research. There are many algorithms presented in literature for video summarization and they represent visual information of video in concise form. Dynamic summaries are constructed with collection of key frames or some smaller segments extracted from video and is presented in the form of small video clip. This paper describes an algorithm for constructing the dynamic summary of a video by modeling every 40 consecutive frames of video as a bipartite graph. The method considers every 20 consecutive frames from video as one set and next 20 consecutive frames as second set of bipartite graph nodes with frames of the video representing nodes of the graph and edges connecting nodes denoting the relation between frames and edge weight depicting the mutual information between frames. Then the minimum edge weight maximal matching in every bipartite graph (a set of pair wise non-adjacent edges) is found using Hungarian method. The frames from the matchings which are represented by the nodes connected by the edges with weight below some empirically defined threshold and two neighbor frames are taken as representative frames to construct the summary. The results of the experiments conducted on data set containing sports videos taken from YOUTUBE and videos of TRECVID MED 2011 dataset have demonstrated the satisfactory average values of performance parameters, namely Informativeness value of 94 % and Satisfaction value of 92 %. These values and duration (MSD) of summaries reveal that the summaries constructed are significantly concise and highly informative and provide highly acceptable dynamic summary of the videos.