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基于支持向量机的语义视频摘要
引用本文:曹建荣,蔡安妮.基于支持向量机的语义视频摘要[J].北京邮电大学学报,2006,29(4):94-98.
作者姓名:曹建荣  蔡安妮
作者单位:北京邮电大学,电信工程学院,北京,100876;山东建筑大学,信息与电气工程学院,济南,250101;北京邮电大学,电信工程学院,北京,100876
摘    要:针对如何在语义层次上形成视频摘要问题,提出了一个基于支持向量机的风光记录片语义视频摘要算法。利用支持向量机对镜头关键帧进行语义分类,对每类镜头关键帧根据引入的镜头“重要性”函数提取构建视频摘要的帧。改变重要性函数阈值的大小,可以很方便的得到不同粒度的视频摘要。实验结果表明该算法形成的视频摘要较好地表达了视频的内容。

关 键 词:支持向量机  语义  视频摘要
文章编号:1007-5321(2006)04-0094-05
收稿时间:2005-07-05
修稿时间:2005年7月5日

Semantic Video Summarization based on Support Vector Machine
CAO Jian-rong,CAI An-ni.Semantic Video Summarization based on Support Vector Machine[J].Journal of Beijing University of Posts and Telecommunications,2006,29(4):94-98.
Authors:CAO Jian-rong  CAI An-ni
Affiliation:1. School of Telecommunication Engineering , Beijing University of Posts and Telecommunications, Beijing 100876,China;
2. School of Information and Electrical Engineering , Shandong Jianzhu University , Jinan 250101, China
Abstract:Constructing the video summarization in semantic level is very important.An algorithm of video summarization based on support vector machine(SVM) in semantic level for the natural scenery documentary is presented.The shot key frames are classified by SVM and the frames constructing the video summarization are selected from the shot key frames of every class by the importance function introduced.The scalable video summarization from coarse to fine level of detail can be achieved by changing the threshold of important function.Experiment results indicate that the proposed algorithm performs satisfactorily.
Keywords:support vector machine  semantic  video summarization
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