关键词: 视频摘要, 视频浏览, 最大边界相关, 流形排序

Abstract: The dramatic increase of video data makes it urgent to efficiently browse and understand video content. Video summarization is such a technology, which presents important and representative video content in a concise form, and makes it easy to browse and manage video. The ideal video summarization should meet the criteria of maximal coverage, important priority and minimal redundancy. However, the main technical challenge of the current video summarization is how to integrate the three standards into a framework to get the ideal video summarization. To overcome this problem, this paper proposes the maximal marginal importance and coverage algorithm (MMIC) for optimizing the redundancy, importance and coverage rate. In MMIC, K-RNN (K-regular nearest neighbor) graph based manifold ranking algorithm is used to find the important video frames. In addition, a summarization coverage criterion (SCC) is presented to intuitively guide the user to obtain appropriate summarization length. Extensive experiments are conducted on Open Video Project and YouTube datasets, and the results show the effectiveness and superiority of MMIC algorithm.

Key words: video summarization, video browsing, maximal marginal relevance, manifold ranking