Graph-Based Hierarchical Video Summarization Using Global Descriptors

Video summarization is a simplification of video content for compacting the video information. The video summarization problem can be transformed to a clustering problem, in which some frames are selected to saliently represent the video content. In this work, we use a hierarchical graph-based clustering method for computing a video summary. In fact, the proposed approach, called Summary, adopts a hierarchical clustering method to generate a weight map from the frame similarity graph in which the clusters (or connected components of the graph) can easily be inferred. Moreover, the use of this strategy allows to apply a similarity measure between clusters during graph partition, instead of considering only the similarity between isolated frames. Furthermore, a new evaluation measure that assesses the diversity of opinions of user summaries, called Covering, is also proposed. Experimental results provide quantitative and qualitative comparison between the new approach and other popular algorithms from the literature, showing that the new algorithm is robust and efficient. Concerning quality measures, Summary outperforms the compared methods regardless of the visual feature used in terms of F-measure.