Publication Type

Journal Article

Version

publishedVersion

Publication Date

2-2005

Abstract

In this paper, we propose a unified approach for video summarization based on the analysis of video structures and video highlights. Two major components in our approach are scene modeling and highlight detection. Scene modeling is achieved by normalized cut algorithm and temporal graph analysis, while highlight detection is accomplished by motion attention modeling. In our proposed approach, a video is represented as a complete undirected graph and the normalized cut algorithm is carried out to globally and optimally partition the graph into video clusters. The resulting clusters form a directed temporal graph and a shortest path algorithm is proposed to efficiently detect video scenes. The attention values are then computed and attached to the scenes, clusters, shots, and subshots in a temporal graph. As a result, the temporal graph can inherently describe the evolution and perceptual importance of a video. In our application, video summaries that emphasize both content balance and perceptual quality can be generated directly from a temporal graph that embeds both the structure and attention information.

Keywords

attention model, normalized cut, scene modeling, video summarization

Discipline

Computer Sciences | Graphics and Human Computer Interfaces

Research Areas

Intelligent Systems and Optimization

Publication

IEEE Transactions on Circuits and Systems for Video Technology

Volume

15

Issue

2

First Page

296

Last Page

305

ISSN

1051-8215

Identifier

10.1109/TCSVT.2004.841694

Publisher

Institute of Electrical and Electronics Engineers

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