Unsupervised video-shot segmentation and model-free, anchorperson detection for news video story parsing

被引：81

作者：

Gao, XB ^{[1
]}

Tang, X ^{[1
]}

机构：

[1] Chinese Univ Hong Kong, Dept Informat Engn, Shatin, Hong Kong, Peoples R China

来源：

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY | 2002年 / 12卷 / 09期

关键词：

anchorperson detection; fuzzy clustering; graphtheoretical; cluster analysis; video library;

D O I：

10.1109/TCSVT.2002.800510

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

News story parsing is an important and challenging task in a news video library system. In this paper, we address two important components in a news video story parsing system: shot boundary detection and anchorperson detection. First, an unsupervised fuzzy c-means algorithm is used to detect video-shot boundaries in order to segment a news video into video shots. Then, a graph-theoretical cluster analysis algorithm is implemented to classify the video shots into anchorperson shots and news footage shots. Because of its unsupervised nature, the algorithms require little human intervention. The efficacy of the proposed method is extensively tested on more than 5 h of news programs.

引用

页码：765 / 776

页数：12

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