Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2005

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.

Keywords

News videos, optimal matching, algorithms

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Intelligent Systems and Optimization

Publication

Image and Video Retrieval: 4th International Conference, CIVR 2005, Singapore, July 20-22: Proceedings

Volume

3568

First Page

257

Last Page

266

ISBN

9783540316787

Identifier

10.1007/11526346_29

Publisher

Springer

City or Country

Cham

Additional URL

https://doi.org/10.1007/11526346_29

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