Hot event detection and summarization by graph modeling and matching

  • Authors:

  • Yuxin Peng;Chong-Wah Ngo

  • Affiliations:

  • Institute of Computer Science and Technology, Peking University, Beijing, China;Department of Computer Science, City University of Hong Kong, Kowloon, HongKong

  • Venue:
  • CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
  • Year:
  • 2005

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Abstract

This paper proposes a new approach for hot event detection and summarization of news videos. The approach is mainly based on two graph algorithms: optimal matching (OM) and normalized cut (NC). Initially, OM is employed to measure the visual similarity between all pairs of events under the one-to-one mapping constraint among video shots. Then, news events are represented as a complete weighted graph and NC is carried out to globally and optimally partition the graph into event clusters. Finally, based on the cluster size and globality of events, hot events can be automatically detected and selected as the summaries of news videos across TV stations of various channels and languages. Our proposed approach has been tested on news videos of 10 hours and has been found to be effective.