Combining graph connectivity & dominant set clustering for video summarization

The paper presents an automatic video summarization technique based on graph theory methodology and the dominant sets clustering algorithm. The large size of the video data set is handled by exploiting the connectivity information of prototype frames that are extracted from a down-sampled version of the original video sequence. The connectivity information for the prototypes which is obtained from the whole set of data improves video representation and reveals its structure. Automatic selection of the optimal number of clusters and hereafter keyframes is accomplished at a next step through the dominant set clustering algorithm. The method is free of user-specified modeling parameters and is evaluated in terms of several metrics that quantify its content representational ability. Comparison of the proposed summarization technique to the Open Video storyboard, the Adaptive clustering algorithm and the Delaunay clustering approach, is provided.