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Hot Event Detection and Summarization by Graph Modeling and Matching
[chapter]
2005
Lecture Notes in Computer Science
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.
doi:10.1007/11526346_29
fatcat:rp7bbzirmvctxag3qnjg4rn4tq