Publication Type

Journal Article

Version

publishedVersion

Publication Date

5-2006

Abstract

This paper proposes a new approach and algorithm for the similarity measure of video clips. The similarity is mainly based on two bipartite graph matching algorithms: maximum matching (MM) and optimal matching (OM). MM is able to rapidly filter irrelevant video clips, while OM is capable of ranking the similarity of clips according to visual and granularity factors. We apply the similarity measure for two tasks: retrieval and summarization. In video retrieval, a hierarchical retrieval framework is constructed based on MM and OM. The validity of the framework is theoretically proved and empirically verified on a video database of 21 h. A query-dependent clip segmentation algorithm is also proposed to automatically locate the potential boundaries of clips in videos. In video summarization, a graph-based clustering algorithm, incorporated with the proposed similarity measure, is adopted to detect the highlighted events reported by different newscasts.

Keywords

clip similarity, query-based segmentation, hierarchical video retrieval, summarization

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Intelligent Systems and Optimization

Publication

IEEE Transactions on Circuits and Systems for Video Technology

Volume

16

Issue

5

First Page

612

Last Page

627

ISSN

1051-8215

Identifier

10.1109/TCSVT.2006.873157

Publisher

Institute of Electrical and Electronics Engineers

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