基于图像识别的电视广告监播系统
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摘要
随着经济的发展,电视广告成为社会生活中越来越重要的一部分,而其带来的社会问题也日渐显著,特别是虚假广告严重误导了消费者,坑害了广大人民,因此广告监测成为社会急需处理的问题。本设计介绍了一种基于图像识别的电视广告监播系统。该系统结合图像采集、二值化预处理等技术,以图像模式识别的方式实现对电视广告的监播。
本设计主要研究广告图像识别技术的软件实现。系统使用视频采集卡,采用DirectShow技术对电视信号进行采集并捕捉特定的帧用于后期识别。针对视频广告识别的特点和要求,采用了“首尾重点提取法”关键帧提取方法。
在采集到电视广告关键帧后,为了简化运算,需要对选取的关键帧进行二值化预处理。系统的二值化模块中,采用了对不同亮度情况的图像处理效果都较好,而且算法比较稳定的Otsu算法。
由于需要识别的对象,也就是需要监播的广告是事先已知的,因此可以事先提取样本,采用基于模板的匹配方法进行识别,本设计选择了SSDA算法。由于在识别模块中,待识别的对象都是二值图像,本设计对SSDA算法进行了适当的调整,使其在识别二值图像时效率更高,并且加入了绝对误差值最低累加次数这一限制,以保证图像识别的准确性。
在上述工作的基础上,本课题在VC环境下进行了对指定广告图像的测试实验。通过实验表明,该方法对广告监测有着一定的实用意义。
TV Advertising is an important part of daily life, by which people know what products they like or dislike. But as the important effects it takes on, it is more and more urgent for us to monitor whether the right ads is on TV. This design introduces an automatic picture oriented television advertisement monitoring system. The system implements television advertisements monitoring in the way of pattern recognition together with image capturing and image binarization technologies.
In this paper, the real-time realization of picture oriented Recognition technology on common platform is investigated. The system captures television signal with video capture card by DirectShow technique. Aiming at the characteristic and the request of the television advertisements, the system introduces the way of capturing the key-frames of the beginning and the end mainly.
After the original data collection step, binarizing the captured images is needed to make the following steps more efficient. In the binarization module, the system introduces the Otsu algorithm, which is better effective to different brightness of the image and is steadier.
Since the monitored objects, that is, the advertisements, are known, the system can extract samples of the advertisements, and use algorithms based on template match to recognize these advertisements. SSDA algorithm is adopted in this system. Because the pictures are all binary images, this article adjusts SSDA algorithm so that it can process binary images more efficiently, and an additional limitation, the absolute error accumulation times, are affiliated to insure the recognition correctness.
Based on the working above, a set of picture oriented recognition system and the corresponding demo software are built under VC. The system is examined with some named advertising. The results show that the system has a higher recognition rate.
本设计主要研究广告图像识别技术的软件实现。系统使用视频采集卡,采用DirectShow技术对电视信号进行采集并捕捉特定的帧用于后期识别。针对视频广告识别的特点和要求,采用了“首尾重点提取法”关键帧提取方法。
在采集到电视广告关键帧后,为了简化运算,需要对选取的关键帧进行二值化预处理。系统的二值化模块中,采用了对不同亮度情况的图像处理效果都较好,而且算法比较稳定的Otsu算法。
由于需要识别的对象,也就是需要监播的广告是事先已知的,因此可以事先提取样本,采用基于模板的匹配方法进行识别,本设计选择了SSDA算法。由于在识别模块中,待识别的对象都是二值图像,本设计对SSDA算法进行了适当的调整,使其在识别二值图像时效率更高,并且加入了绝对误差值最低累加次数这一限制,以保证图像识别的准确性。
在上述工作的基础上,本课题在VC环境下进行了对指定广告图像的测试实验。通过实验表明,该方法对广告监测有着一定的实用意义。
TV Advertising is an important part of daily life, by which people know what products they like or dislike. But as the important effects it takes on, it is more and more urgent for us to monitor whether the right ads is on TV. This design introduces an automatic picture oriented television advertisement monitoring system. The system implements television advertisements monitoring in the way of pattern recognition together with image capturing and image binarization technologies.
In this paper, the real-time realization of picture oriented Recognition technology on common platform is investigated. The system captures television signal with video capture card by DirectShow technique. Aiming at the characteristic and the request of the television advertisements, the system introduces the way of capturing the key-frames of the beginning and the end mainly.
After the original data collection step, binarizing the captured images is needed to make the following steps more efficient. In the binarization module, the system introduces the Otsu algorithm, which is better effective to different brightness of the image and is steadier.
Since the monitored objects, that is, the advertisements, are known, the system can extract samples of the advertisements, and use algorithms based on template match to recognize these advertisements. SSDA algorithm is adopted in this system. Because the pictures are all binary images, this article adjusts SSDA algorithm so that it can process binary images more efficiently, and an additional limitation, the absolute error accumulation times, are affiliated to insure the recognition correctness.
Based on the working above, a set of picture oriented recognition system and the corresponding demo software are built under VC. The system is examined with some named advertising. The results show that the system has a higher recognition rate.
引文
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[7]Junsong Yuan,Qi Tian,Ranganath S.Fast and robust search method for short video clips from large video collection.Pattern Recogonition.2004.ICPR 2004.Proceedings of the 17th International Conference,2004:866-869
[8]Jerome Meessen,Matthieu Coulanges,Xavier Desurmont,Jean-Francois Delaigle.Content-based retrieval of video surveillance scenes.Multimedia content representation,Classification and Security(MCRS 2006),September,2006:785-792
[9]卢官明.基于内容的图像及视频检索.南京邮电学报,2002,22(2):23-26
[10]Zhuang Y T,Rui Y,Huang T S,et al.Adaptive key frame extracting using unsupervised clustering.Chicago:Proceedings of the 1998 IEEE International Conference on Image Processing,2000:884-887
[11]W Wolf.Key frame Selection by motion analysis[C].In:Proceedings of IEEE International Conference On Acoustics,Speech,and Signal Processing,Atlanta ICASSP,1996:228-1231
[12]Ferman A M,Tekalp A M.Multiscale Content Extraction and Representation for Video Indexing.Multimedia Storage and Archival Systems(Dallas,TX),1997,11
[13]王红梅,张科.图像匹配研究进展,计算机工程与应用,2004,40(19):42-44
[14]李弼程,彭天强.智能图像处理技术.电子工业出版社,2004:2-8
[15]Shi-Kuo Chang,Yin-Wah.Optimal histogram matching by monotone gray level transformation.Communications of the ACM,1978,21(10):835-840
[16]Brown L G.A survey of image registration techniques.ACM Computing Surveys,1992,24(4):325
[17]陈振羽,周焰.关于计算机图像理解的知识与知识表达.红外与激光工程,2000,29(1):73-77
[18]Wenhao Wang,Yungchang Chen.Image registration by control points pairing using the invariant properties of line segments.Pattern Recognition letters,1997,18(3):269
[19]Corvi M,Nicchiotti G.Multiresolution image registration.Proc of 1995 International Conference on Image Processing,1995:224-227
[20]Stockman C L.Matching images to models for registration and object detection via clustering.IEEE Trans,on PAMI,1982,4(3):229-230
[21]Dana K,Anandan P,Registration of visible and infrared images.SPIE,1993,1957:2-17
[22]Hui Li,Manjunath B S.A contour-based approach to multisensor image registration.IEEE Trans,on image Processing,1995,4(3):320-334
[23]Bourret P.Cabon B,A neural approach for satellite image registration and pairing segmented areas.SPIE,1995(2579):229
[24]Unser M,Aldroubi A,A multiresolution image registration procedure using spline pyramids.SPIE,1993(2034):160
[25]Djamdji J.P.Geometrical registration of images:The multiresolution approach.Photogrammetry and Remote Sensing Journal,1993,59(5):645-653
[26]Jun-Wei Hsieh,Image registration using a new edge-based approach.Computer Vision and Image Understanding,1997,67(2):112-130
[27]黄知义,周宁.基于内容视频检索的关键技术研究.现代情报,2005,10:126-129
[28]林通,张宏江.镜头内容分析及其在视频检索中的应用.软件学报,2002,13(04):1577-1585
[29]陆其明.DirectShow实务精选[M].北京:科学出版社.2004
[30]王锋.基于DirectShow数字视频远程战场监控系统的设计.重庆大学工程硕士学位论文,2005:11-13
[31]姜中华,师鸣若.基于DirectShow的通用多媒体帧提取和应用.襄樊学院学报,2003.9:42-43
[32]杨淑莹.VC++图像处理程序设计.北京:清华大学出版社;北京交通大学出版社,2003:1-3
[33]徐慧.Visual C++数字图像实用工程案例精选.北京:人民邮电出版社,2004:2-4
[34]何斌,马天予等.Visual C++数字图像处理.人民邮电出版社,2001:35-39
[35]田村秀行.计算机图像处理.北京:科学出版社,2004:151-180
[36]Gonzalez R,Woods R.Digital Image Processing,Addison-Wesley Publishing Company,1999:144-192
[37]赵善龙,刘明勇.图像二值化时阈值自适应选取方法及其Visual C++实现.哈尔滨铁道科技,2006:8-10
[38]Raymond T Ng,Jiawei Han.Efficient and effective clustering methods for spatial data mining.Proceeding of the 20~(th)International Conference on Very Large Data Bases.Santiago,Chile,2002:144-155
[39]叶芗芸,戚飞虎.文本图像的二值化方法.红外与毫米波学报,1997,16(5):344-350
[40]马驰,张红云.改进的多阈值动态二值化算法.计算机工程,2006,32(6):1-3
[41]N.Otsu,A threshold selection method from gray-level histogram.IEEE Trans systems Man and Cybemet,1989,(8):62-66
[42]庄军,李弼程.一种有效的文本图像二值化方法.微计算机信息,2005,21(8):38-42
[43]王立新,刘彤宇.SSDA图像匹配算法的研究及实现.光电技术应用,2005,20(3):98-100
[44]叶良,蒋凯男,高建民,刘仲良.VC中的ADO开发简介.计算机时代,2002(8):20-21
[45]陈海东,葛姚.基于语音识别的计算机广告自动监播系统.电脑开发与应用,2007:24-29
[46]陈海东,葛姚.基于图像识别的电视广告监播系统.电脑与电信,2006:25-28
[47]于立洋,刘胜辉.基于图像识别技术的电视广告监播系统.黑龙江科技信息,2007:25
[48]刘小石,郑淮,马林伟,杨帆.精通Visual C++6.0.北京:清华大学出版社,2000
[49]陆宗骐.C/C++图像处理编程.北京:清华大学出版社,2005
[2]马彬.浅谈有线电视播出信号实时监测系统的运用.有线电视技术,2005(18):95-96
[3]蔡芸.记忆的规律与广告的策略.商业研究,1999,1:82-85
[4]李涛.电视广告计算机监审管理系统的研制与应用.广播与电视技术,2005,32(1):25-27
[5]李霞.广电行业存储应用分析和方案——浪潮针对广播电视的存储解决方案.中国招标,2005,5:70-71
[6]N Diakopoulos,S Volmer.Temporally tolerant video matching.SIGIR Multimedia Information Retrieval Workshop,2003:246-252
[7]Junsong Yuan,Qi Tian,Ranganath S.Fast and robust search method for short video clips from large video collection.Pattern Recogonition.2004.ICPR 2004.Proceedings of the 17th International Conference,2004:866-869
[8]Jerome Meessen,Matthieu Coulanges,Xavier Desurmont,Jean-Francois Delaigle.Content-based retrieval of video surveillance scenes.Multimedia content representation,Classification and Security(MCRS 2006),September,2006:785-792
[9]卢官明.基于内容的图像及视频检索.南京邮电学报,2002,22(2):23-26
[10]Zhuang Y T,Rui Y,Huang T S,et al.Adaptive key frame extracting using unsupervised clustering.Chicago:Proceedings of the 1998 IEEE International Conference on Image Processing,2000:884-887
[11]W Wolf.Key frame Selection by motion analysis[C].In:Proceedings of IEEE International Conference On Acoustics,Speech,and Signal Processing,Atlanta ICASSP,1996:228-1231
[12]Ferman A M,Tekalp A M.Multiscale Content Extraction and Representation for Video Indexing.Multimedia Storage and Archival Systems(Dallas,TX),1997,11
[13]王红梅,张科.图像匹配研究进展,计算机工程与应用,2004,40(19):42-44
[14]李弼程,彭天强.智能图像处理技术.电子工业出版社,2004:2-8
[15]Shi-Kuo Chang,Yin-Wah.Optimal histogram matching by monotone gray level transformation.Communications of the ACM,1978,21(10):835-840
[16]Brown L G.A survey of image registration techniques.ACM Computing Surveys,1992,24(4):325
[17]陈振羽,周焰.关于计算机图像理解的知识与知识表达.红外与激光工程,2000,29(1):73-77
[18]Wenhao Wang,Yungchang Chen.Image registration by control points pairing using the invariant properties of line segments.Pattern Recognition letters,1997,18(3):269
[19]Corvi M,Nicchiotti G.Multiresolution image registration.Proc of 1995 International Conference on Image Processing,1995:224-227
[20]Stockman C L.Matching images to models for registration and object detection via clustering.IEEE Trans,on PAMI,1982,4(3):229-230
[21]Dana K,Anandan P,Registration of visible and infrared images.SPIE,1993,1957:2-17
[22]Hui Li,Manjunath B S.A contour-based approach to multisensor image registration.IEEE Trans,on image Processing,1995,4(3):320-334
[23]Bourret P.Cabon B,A neural approach for satellite image registration and pairing segmented areas.SPIE,1995(2579):229
[24]Unser M,Aldroubi A,A multiresolution image registration procedure using spline pyramids.SPIE,1993(2034):160
[25]Djamdji J.P.Geometrical registration of images:The multiresolution approach.Photogrammetry and Remote Sensing Journal,1993,59(5):645-653
[26]Jun-Wei Hsieh,Image registration using a new edge-based approach.Computer Vision and Image Understanding,1997,67(2):112-130
[27]黄知义,周宁.基于内容视频检索的关键技术研究.现代情报,2005,10:126-129
[28]林通,张宏江.镜头内容分析及其在视频检索中的应用.软件学报,2002,13(04):1577-1585
[29]陆其明.DirectShow实务精选[M].北京:科学出版社.2004
[30]王锋.基于DirectShow数字视频远程战场监控系统的设计.重庆大学工程硕士学位论文,2005:11-13
[31]姜中华,师鸣若.基于DirectShow的通用多媒体帧提取和应用.襄樊学院学报,2003.9:42-43
[32]杨淑莹.VC++图像处理程序设计.北京:清华大学出版社;北京交通大学出版社,2003:1-3
[33]徐慧.Visual C++数字图像实用工程案例精选.北京:人民邮电出版社,2004:2-4
[34]何斌,马天予等.Visual C++数字图像处理.人民邮电出版社,2001:35-39
[35]田村秀行.计算机图像处理.北京:科学出版社,2004:151-180
[36]Gonzalez R,Woods R.Digital Image Processing,Addison-Wesley Publishing Company,1999:144-192
[37]赵善龙,刘明勇.图像二值化时阈值自适应选取方法及其Visual C++实现.哈尔滨铁道科技,2006:8-10
[38]Raymond T Ng,Jiawei Han.Efficient and effective clustering methods for spatial data mining.Proceeding of the 20~(th)International Conference on Very Large Data Bases.Santiago,Chile,2002:144-155
[39]叶芗芸,戚飞虎.文本图像的二值化方法.红外与毫米波学报,1997,16(5):344-350
[40]马驰,张红云.改进的多阈值动态二值化算法.计算机工程,2006,32(6):1-3
[41]N.Otsu,A threshold selection method from gray-level histogram.IEEE Trans systems Man and Cybemet,1989,(8):62-66
[42]庄军,李弼程.一种有效的文本图像二值化方法.微计算机信息,2005,21(8):38-42
[43]王立新,刘彤宇.SSDA图像匹配算法的研究及实现.光电技术应用,2005,20(3):98-100
[44]叶良,蒋凯男,高建民,刘仲良.VC中的ADO开发简介.计算机时代,2002(8):20-21
[45]陈海东,葛姚.基于语音识别的计算机广告自动监播系统.电脑开发与应用,2007:24-29
[46]陈海东,葛姚.基于图像识别的电视广告监播系统.电脑与电信,2006:25-28
[47]于立洋,刘胜辉.基于图像识别技术的电视广告监播系统.黑龙江科技信息,2007:25
[48]刘小石,郑淮,马林伟,杨帆.精通Visual C++6.0.北京:清华大学出版社,2000
[49]陆宗骐.C/C++图像处理编程.北京:清华大学出版社,2005