A new method for shot gradual transiton detection using support vector machine

被引：0

作者：

Ling, J ^{[1
]}

Lian, YQ ^{[1
]}

Zhuang, YT ^{[1
]}

机构：

[1] Zhejiang Univ, Inst Artificial Intelligence, Hangzhou 310027, Peoples R China

来源：

PROCEEDINGS OF 2005 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-9 | 2005年

关键词：

variance projection function; gradual transition detection; video similarity; support vector machine;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The detection of gradual transition is much more difficult than that of abrupt transition. In this paper, a new method for gradual transition detection that applies support vector machine is proposed. First, an improved variance projection function is introduced, and its practicality to the detection of gradual transition is analyzed as well. Then by using this variance projection function, the distance between the video frames is defined, and a method to calculate the feature vector of changes of the distance is proposed. Finally, a statistical learning method based on the support vector machine is devised to determine whether the changes of the distance are caused by gradual transition or not. The experiments results show that this method has better detection resolution and less timing complexity, and thus satisfactorily meets the requirements of real-time video-shot detection.

引用

页码：5599 / 5604

页数：6

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