Multi-View Video Summarization

Previous video summarization studies focused on monocular videos, and the results would not be good if they were applied to multi-view videos directly, due to problems such as the redundancy in multiple views. In this paper, we present a method for summarizing multi-view videos. We construct a spatio-temporal shot graph and formulate the summarization problem as a graph labeling task. The spatio-temporal shot graph is derived from a hypergraph, which encodes the correlations with different attributes among multi-view video shots in hyperedges. We then partition the shot graph and identify clusters of event-centered shots with similar contents via random walks. The summarization result is generated through solving a multi-objective optimization problem based on shot importance evaluated using a Gaussian entropy fusion scheme. Different summarization objectives, such as minimum summary length and maximum information coverage, can be accomplished in the framework. Moreover, multi-level su...

• Type: Journal
• Year: 2010
• Where: TMM
• Authors: Yanwei Fu, Yanwen Guo, Yanshu Zhu, Feng Liu, Chuanming Song, Zhi-Hua Zhou
• Keywords: Management, Multi-view Video, Shot Graph, Spatio-temporal Shot Graph, TMM 2010