基于内容的新闻视频检索
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摘要
随着互联网和数字媒体技术的发展以及海量数字视频的出现,研究如何从大量数字视频中获取用户感兴趣的信息的基于内容的视频检索(CBVR)技术成为计算机科学研究的重点课题。本文以新闻视频为研究对象,根据新闻视频其自身特殊的结构、组织和检索方式,对已有的CBVR技术进行了改进,实现对新闻视频检索系统检索功能的改善。本文研究工作如下：
(1)提出了一种改进的镜头边界检测算法。在完成对视频的结构化分析及关键技术的研究工作中,比较了像素比较镜头边界检测法、基于图像边缘轮廓特征镜头边界检测法、灰度差值法、直方图镜头检测法以及双直方图镜头检测法,权衡各种算法的优缺点,提出了基于像素比较法、直方图法、双直方图法互补性特点的一种改进的镜头边界检测方法。
(2)提出了固定区域模板匹配法用于对新闻视频主持人镜头进行检测的方法。在新闻视频结构化特征提取部分,首先通过扩展人脸区域,即EFR方法选取标准主持人镜头作为通用模板,并由此确定出视频的检索区域,然后采用分块HSV颜色直方图作为模板参数,对初步选定的镜头的关键帧进行模板匹配实现主持人镜头的最终确认。该方法既能保证系统检测的灵活性,又能降低误检率。
(3)构造了新闻视频语义分析提取原型系统。实现了对视频关键帧的语义分析、视频镜头的语义分析和视频场景的分割及语义提取。在完成新闻视频镜头分割、主持人镜头检测、字幕检测和广告视频场景分割的基础上,根据新闻视频中音频信息的特点,结合提取的音频语义构造了新闻视频场景分割及自动生成语义系统。该系统为用户查找视频进行视频检索提供了有力的技术支持,具有较高的时效性。
With the development of the Internet and digital media technology and the emergence of mass digital video, we need to facilitate access to a large number of video information in video information of interest, Thus, content-based video retrieval(CBVR) become an important issue in the field of computer science. This paper choose news videos as its research object and is based news videos'features of special structure,organization and retrieval way,which make it one of the most important topics。We have do research as follow.
(1) This put forward a improved the shot boundary detection algorithm. Upon completion of the structure analysis of video and key technology research work, compared method of compares the pixel, edge profile characteristic method, gray level difference method, and dual histogram histogram shot detection method, We propose an improved method for shot boundary detection, based the complementary characteristics of pixel comparison, the histogram method and double histogram.
(2) In news feature extraction part of this paper, a fixed area template matching shot detection for news video host is proposed. First of all standards host lens is detected by EFR Methods and thus determine the video search area, with block HSV color histogram as a template parameter, on behalf of all the possible lens frame template matching to achieve the ultimate host Lens confirmed. This approach can guarantee the detection of the flexibility of the system can reduce the false detection rate.
(3) Based on the shot segmentation, shot detection of host, theme subtitle detection, advertising video scene segmentation, audio streams semantic extraction of the division, we determine the completion of the video scene. For video indexing, browsing to provide the organizational framework, the semantic information automatically generated based feature extraction, can quickly point users to find video clips with better efficiency. This comprehensive utilization of features of video and audio and its own model processing of news specific, makes the process steps to clear, the processing results to objective and accurate. With the current outstanding research achievements, this paper can realize content-based video retrieval better.
(1)提出了一种改进的镜头边界检测算法。在完成对视频的结构化分析及关键技术的研究工作中,比较了像素比较镜头边界检测法、基于图像边缘轮廓特征镜头边界检测法、灰度差值法、直方图镜头检测法以及双直方图镜头检测法,权衡各种算法的优缺点,提出了基于像素比较法、直方图法、双直方图法互补性特点的一种改进的镜头边界检测方法。
(2)提出了固定区域模板匹配法用于对新闻视频主持人镜头进行检测的方法。在新闻视频结构化特征提取部分,首先通过扩展人脸区域,即EFR方法选取标准主持人镜头作为通用模板,并由此确定出视频的检索区域,然后采用分块HSV颜色直方图作为模板参数,对初步选定的镜头的关键帧进行模板匹配实现主持人镜头的最终确认。该方法既能保证系统检测的灵活性,又能降低误检率。
(3)构造了新闻视频语义分析提取原型系统。实现了对视频关键帧的语义分析、视频镜头的语义分析和视频场景的分割及语义提取。在完成新闻视频镜头分割、主持人镜头检测、字幕检测和广告视频场景分割的基础上,根据新闻视频中音频信息的特点,结合提取的音频语义构造了新闻视频场景分割及自动生成语义系统。该系统为用户查找视频进行视频检索提供了有力的技术支持,具有较高的时效性。
With the development of the Internet and digital media technology and the emergence of mass digital video, we need to facilitate access to a large number of video information in video information of interest, Thus, content-based video retrieval(CBVR) become an important issue in the field of computer science. This paper choose news videos as its research object and is based news videos'features of special structure,organization and retrieval way,which make it one of the most important topics。We have do research as follow.
(1) This put forward a improved the shot boundary detection algorithm. Upon completion of the structure analysis of video and key technology research work, compared method of compares the pixel, edge profile characteristic method, gray level difference method, and dual histogram histogram shot detection method, We propose an improved method for shot boundary detection, based the complementary characteristics of pixel comparison, the histogram method and double histogram.
(2) In news feature extraction part of this paper, a fixed area template matching shot detection for news video host is proposed. First of all standards host lens is detected by EFR Methods and thus determine the video search area, with block HSV color histogram as a template parameter, on behalf of all the possible lens frame template matching to achieve the ultimate host Lens confirmed. This approach can guarantee the detection of the flexibility of the system can reduce the false detection rate.
(3) Based on the shot segmentation, shot detection of host, theme subtitle detection, advertising video scene segmentation, audio streams semantic extraction of the division, we determine the completion of the video scene. For video indexing, browsing to provide the organizational framework, the semantic information automatically generated based feature extraction, can quickly point users to find video clips with better efficiency. This comprehensive utilization of features of video and audio and its own model processing of news specific, makes the process steps to clear, the processing results to objective and accurate. With the current outstanding research achievements, this paper can realize content-based video retrieval better.
引文
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[3]李玉峰.基于内容视频检索的镜头检测及场景检测研究[D].天津：天津大学博士学位论文,2009
[4]史迎春.基于内客的视频检索语义提取若干问题研究[D].南京：南京理大学,2005
[5]TimnanigLiu, Hongjiang Zhang, Feihu Qi. A novel video key-frame-extraction algorithm based on Perceived motion energy model. IEEE transactions on circuits and systems for video technology.2003,13(10):1006 - 1013
[6]盛永华.基于内容的图像检索技术[J].信息科学,2010,3：57-58
[7]E. Wold, T. blum, D. Keislar, J. Wheaten. Content-based lassification, seareh, and retrieval of audio. IEEE Multimedia.1996,3(3):27-36.
[8]于海珠,司瑾.基于内容的图像检索技术[J]. 电脑知识与技术,2010,6(10)：2440-2441
[9]梁刘红,富亮,薛向阳.电视节目自动分割算法[J].计算机研究与发展,2004,41(9)：1514-1519
[10]杨强,尹德辉,马森.视频检索技术应用及发展趋势[J].电视技术,2006,31(2)：88-89
[11]焦保军.基于新闻视频字幕的检测与提取分析[D].南京：南京理工大学硕士学位论文,2007
[12]Y Rui, T S huang, S F Chang, Image retrieval:current techniques, Promising directions, and open issues. Journal of Visual communication and image representation,1999,10:39-62
[13]黄祥林.基于压缩域图像检索技术的初步研究[D].北京：北京工业大学博士论文,2001
[14]黄祥林,沈兰荪.基于内容的图像检索技术研究[J].电子学报,2002,30(7)：1065-1071
[15]M J Swain, D H Ballard. Color indexing. Int. Journal of Computer Vision, 1991,7(1):11-32
[16]M Stricker, M Orengo. Similarity of color images. SPIE Storage and Retrieval for image and video Datavbase Ⅲ,1995,2185(2):381-392
[17]H Derek, S Rahul, S Henry, et al. Object-based image retrieval using the statistical structure of images IEEE Computer Society Conference on computer Vision and pattern Recognition, Washington DC, June27-July2,2004, 2(7):490-497
[18]S K Chang. Iconic indexing by 2D string. IEEE trans pattern analysis and machine intelligence,1984,6(4):413-428
[19]卢悦.基于内容的视频镜头检测与分类研究[D].济南：山东大学硕士论文,2010
[20]曹莉华,胡晓峰,李国辉.基于内容检索中的视频处理技术研究[J].计算机工程与应用,1999,(6)：39-55
[21]E. world, T. Blum, D. Keislar,J. Wheaten. Content-based classification, search, and retrieval of audio. IEEE Multimedia.1996,3(3):27-36
[22]R.Lienhart, Comparison of Automatic Shot Boundary Detection Algorithms, SPIE,1999,3656:290-301
[23]黄茜,张海泉,杨文亮.基于灰度和直方图的阈值自适应镜头边界检测[J].科学技术与工程,2007,4(7)：1671-1819
[24]邓雪飞,夏定元.几种改进的镜头边界检测方法[J].数字电视与数字视频2005,7(2)：112-116
[25]朱小俊,老松杨.一种有效的镜头探测方法[J].计算机工程与应用,2003,(8)：59-61
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