光谱成像技术研究室知识库

Multi-Video Summarization (MVS) aims at condensing a large number of web videos by the same search query into a compact storyboard or video skimming. Although video summarization has achieved great progress, most existing work focuses only on extracting a summary from a single video. The genre specific assumptions for Single Video Summarization (SVS) often do not hold for MVS. Moreover, the complex data characteristics among web videos make MVS more challenging. To well address the challenges in MVS, this paper presents a novel framework with Hypergraph Dominant Set (HDS), termed MVS-HDS. Specifically, we first formulate the MVS task into a problem of finding dominant sets in a hypergraph. Then, with the help of web images searched by the same query, a Query Dependent Maximum Marginal Relevance (QD-MMR) method is developed to refine these keyframe candidates by balancing the criteria of conciseness and query adaptation. Finally, to make the summary easy to understand, we further present a Graph-based Topical Closeness (GTC) method to make it more comprehensible. Extensive experiments show that the proposed framework clearly outperforms the state-of-the-art methods. (C) 2018 Published by Elsevier B.V.