基于多种特征的视频分类研究
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摘要
随着计算机技术和多媒体技术的迅猛发展,人们越来越容易制作和存储数字视频,并且在通信与互联网普及的今天,数字视频在网络上的传播也更加容易,在全世界范围内形成了海量的数据库。视频作为声音、图像、文字等信息的载体,给用户展现了不同的信息。人们总是希望从这些海量的视频数据库中搜索出一些有用的信息,找出一些自己感兴趣的视频。这就必须要先对视频进行分类整理,使人们在搜索视频时有一定的规律可循。但是,如何对这些海量的视频数据进行分类整理是视频处理中亟待解决的问题之一。近年来,对视频进行分类逐渐成为了研究的热点,也是极具挑战性的研究课题。
本文对视频分类作了较深入的研究,首先分析了视频分类的研究现状与发展趋势,并在总结现有算法优劣的基础上提出了一种通过提取多种特征,利用基于主动相关反馈的支持向量机(sVM)实现体育视频分类的方法。
不同类别的体育视频在场地颜色,场地位置,区域亮度,纹理,运动强度及运动员的运动方式和运动区域上都有一定的区别。因此,可通过提取这些方面的特征对视频进行刻画,用以表示视频信息。文中提出了一种基于区域的视频特征提取方法,首先将视频按区域分块,再计算视频关键帧中各块的颜色矩作为颜色特征,并对块之间亮度均值进行比较得到块亮度比较编码(BICC)作为亮度特征,其次提取视频各个区域中的运动强度,运动方向等信息作为运动特征,再通过关键帧的灰度共生矩阵提取出纹理特征。为了提高处理效率,通过主成分分析法(PCA)对特征进行降维处理。本文在此基础上设计了一个基于主动相关反馈的SVM树型多分类模型用于视频分类,在模型的每个分类节点中,都使用一个或多个SVM二类分类器,并通过投票法对每个节点的分类结果进行统计以进行视频样本类别属性的判定。最后利用搜集的视频进行测试,实验结果表明,本文提出的通过多种特征及基于主动相关反馈的SVM树型分类模型实现视频分类的方法,具有良好的性能。
With the rapid development of computer and multimedia technique, it is possible to make and store digital videos easily, so the digital videos become an appropriate source of information for various users like researchers. Additionally, Current information and communication technologies provide the infrastructure to transport bits anywhere. But it is difficult for users to search the videos in which they are interested from the mountains of video databases. In other words, many of these videos recording data are currently hardly usable, and this is mainly due to the lack of appropriate techniques, which can make the video content more accessible. So with both the rapid increase in the amount of generated video data and the wide range of video applications, an efficient and effective management of video records is much demanded. Manually indexing video content is currently the most accurate method, however, it is a very time-consuming process. For an user to retrieve the required information, automatic classification and categorization of the video content is essential.
This dissertation makes a deep research on video automatic classification including analysis the state of the art, and summarizes the progress trend and drawbacks or advantages of these existing algorithms. Then we propose a new algorithm about video automatic classification based on various features and support vector machine (SVM) based on active relevance feedback.
Considering that the average intensity, motion intensity and color distribution are different among regions of video, we propose a novel feature extracted method based on video regions. First, it divides the video into blocks, then according to comparison of the average intensity among different blocks of key frames to get the feature block intensity comparison code (BICC), and get the block color histogram through the statistics of color components in each block and extract the texture of key frames. Then extract the motion intensity of every region of videos. Furthermore, using principal component analysis (PCA), the extracted features are reduced the redundancy while exploiting the correlations between the feature elements. Finally, we design a tree classification model based on SVM with active relevance feedback, and use it to classify the videos with extracted features. The experimental results show that the proposed approach in the dissertation outperforms other methods which are based on features such as video saliency regions or only BICC.
本文对视频分类作了较深入的研究,首先分析了视频分类的研究现状与发展趋势,并在总结现有算法优劣的基础上提出了一种通过提取多种特征,利用基于主动相关反馈的支持向量机(sVM)实现体育视频分类的方法。
不同类别的体育视频在场地颜色,场地位置,区域亮度,纹理,运动强度及运动员的运动方式和运动区域上都有一定的区别。因此,可通过提取这些方面的特征对视频进行刻画,用以表示视频信息。文中提出了一种基于区域的视频特征提取方法,首先将视频按区域分块,再计算视频关键帧中各块的颜色矩作为颜色特征,并对块之间亮度均值进行比较得到块亮度比较编码(BICC)作为亮度特征,其次提取视频各个区域中的运动强度,运动方向等信息作为运动特征,再通过关键帧的灰度共生矩阵提取出纹理特征。为了提高处理效率,通过主成分分析法(PCA)对特征进行降维处理。本文在此基础上设计了一个基于主动相关反馈的SVM树型多分类模型用于视频分类,在模型的每个分类节点中,都使用一个或多个SVM二类分类器,并通过投票法对每个节点的分类结果进行统计以进行视频样本类别属性的判定。最后利用搜集的视频进行测试,实验结果表明,本文提出的通过多种特征及基于主动相关反馈的SVM树型分类模型实现视频分类的方法,具有良好的性能。
With the rapid development of computer and multimedia technique, it is possible to make and store digital videos easily, so the digital videos become an appropriate source of information for various users like researchers. Additionally, Current information and communication technologies provide the infrastructure to transport bits anywhere. But it is difficult for users to search the videos in which they are interested from the mountains of video databases. In other words, many of these videos recording data are currently hardly usable, and this is mainly due to the lack of appropriate techniques, which can make the video content more accessible. So with both the rapid increase in the amount of generated video data and the wide range of video applications, an efficient and effective management of video records is much demanded. Manually indexing video content is currently the most accurate method, however, it is a very time-consuming process. For an user to retrieve the required information, automatic classification and categorization of the video content is essential.
This dissertation makes a deep research on video automatic classification including analysis the state of the art, and summarizes the progress trend and drawbacks or advantages of these existing algorithms. Then we propose a new algorithm about video automatic classification based on various features and support vector machine (SVM) based on active relevance feedback.
Considering that the average intensity, motion intensity and color distribution are different among regions of video, we propose a novel feature extracted method based on video regions. First, it divides the video into blocks, then according to comparison of the average intensity among different blocks of key frames to get the feature block intensity comparison code (BICC), and get the block color histogram through the statistics of color components in each block and extract the texture of key frames. Then extract the motion intensity of every region of videos. Furthermore, using principal component analysis (PCA), the extracted features are reduced the redundancy while exploiting the correlations between the feature elements. Finally, we design a tree classification model based on SVM with active relevance feedback, and use it to classify the videos with extracted features. The experimental results show that the proposed approach in the dissertation outperforms other methods which are based on features such as video saliency regions or only BICC.
引文
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[16]Jinhui Yuan, Huiyi Wang, Lan Xiao, Wujie Zheng, Jianmin Li, Fuzong Lin, Bo Zhang,; "A Formal Study of Shot Boundary Detection" [J], IEEE Trans. Circuits Syst. Video Technol,2007, 2(17):168-186.
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[24]蒋艳凰,赵强利，“机器学习方法”[M],北京：电子工业出版社,2009。
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[27]Daniel Hemandez-Lobato, Jose Miguel Hernandez-Lobato, "Bayes Machines for binary classification"[J], Pattern Recognition Letters,2008,29:1466-1473.
[28]Marco Ramoni, Paola Sebastiani, "Robust Bayes classifiers" [J], Artificial Intelligence,2001, 125:209-226.
[29]Yingquan Wu, Krassimir Ianakiev, Venu Govindaraju, "Improved k-nearest neighbor classification" [J], Pattern Recognition,2002,35:2311-2318.
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