基于颜色与形状特征的图像检索技术研究
|
摘要
数字图像作为重要的多媒体信息载体之一,由于其包含的信息量大,具有文字所不能替代的优势,得到越来越广泛的应用。对庞大的数字图像集合进行有效的利用和管理成为近年来的一个研究热点。基于内容的图像检索(CBIR)就是利用图像本身具有的内容,如颜色,形状,纹理等特征完成图像匹配,快速有效地找到目标图像的技术。本文对颜色特征,形状特征及相关反馈几个方面进行了研究,实现了基于内容的图像检索。
本文提出一种基于颜色特征及形状特征的两层图像检索算法。论文的主要工作和成果如下：
1、对图像信息特征提取方法进行了研究。分别介绍了颜色、纹理、形状特征表述方法以及相似性度量匹配等。本文算法选取全局及局部颜色直方图来表述图像颜色特征。既利用了全局颜色直方图便于提取、计算的优点,又应用了局部颜色直方图包含图像空间分布信息的优点。
2、利用小波变换具有的良好局部分析特性用于获取图像信息。本文应用二维小波的多尺度分析以及小波变换的模极大值提取了图像目标的边缘形状特征,并利用七阶不变矩得到形状边缘特征的描述。
3、针对图像检索相关反馈技术进行研究。将支持向量机(SVM)理论应用在图像检索相关反馈中,并对SVM参数选择对反馈结果的影响进行了讨论。
实验结果表明,两层图像检索算法结合相关反馈技术的应用能取得良好的检索效果。
As a kind of important multimedia information carriers, the digital image is more and more widespread for it contains the huge information and has the superiority which the text can not substitute.How to use and manage the huge digital image set effectively has become a research hot spot in the recent years. The content-based image retrieval (CBIR) which is a technology matches the similar image effectively and rapidly from the image target set according to the intrinsic feature or content such as color, sharp, texture etc. This thesis researches the image color features, shape features and relevance feedback technology, and achieves the content-based image retrieval.
This thesis proposes a kind of two-level image retrieval algorithm based on the color features and the shape features. The main research work and production are as follows:
1、Research on the methods of image feature extraction. We introduce several kinds of feature expressions of color, texture and shape. The proposed algorithm of the thesis uses the global and local spatial color histograms to describe the image color features. It is not only using simple calculation and fast retrieval advantage of the global color histograms, but including the image spatial distribution information in the local color histograms.
2、Research on wavelet transformation for extracting the image features. The wavelet transformation has the good partial analysis feature. This thesis introduces the method to extract the edge shape feature of the image object, which uses two-dimensional multi-scale wavelet analysis and the wavelet transform modulus maxima value and describes the image shape feature by the seven invariant moments.
3、Research on the image retrieval relevance feedback. Apply support vector machines (SVM) theory to image retrieval relevance feedback and discuss the influence of the choice of different kernel functions.
The experimental results show that the two-level image retrieval algorithm combined with the relevance feedback technology can get a better retrieval results.
本文提出一种基于颜色特征及形状特征的两层图像检索算法。论文的主要工作和成果如下：
1、对图像信息特征提取方法进行了研究。分别介绍了颜色、纹理、形状特征表述方法以及相似性度量匹配等。本文算法选取全局及局部颜色直方图来表述图像颜色特征。既利用了全局颜色直方图便于提取、计算的优点,又应用了局部颜色直方图包含图像空间分布信息的优点。
2、利用小波变换具有的良好局部分析特性用于获取图像信息。本文应用二维小波的多尺度分析以及小波变换的模极大值提取了图像目标的边缘形状特征,并利用七阶不变矩得到形状边缘特征的描述。
3、针对图像检索相关反馈技术进行研究。将支持向量机(SVM)理论应用在图像检索相关反馈中,并对SVM参数选择对反馈结果的影响进行了讨论。
实验结果表明,两层图像检索算法结合相关反馈技术的应用能取得良好的检索效果。
As a kind of important multimedia information carriers, the digital image is more and more widespread for it contains the huge information and has the superiority which the text can not substitute.How to use and manage the huge digital image set effectively has become a research hot spot in the recent years. The content-based image retrieval (CBIR) which is a technology matches the similar image effectively and rapidly from the image target set according to the intrinsic feature or content such as color, sharp, texture etc. This thesis researches the image color features, shape features and relevance feedback technology, and achieves the content-based image retrieval.
This thesis proposes a kind of two-level image retrieval algorithm based on the color features and the shape features. The main research work and production are as follows:
1、Research on the methods of image feature extraction. We introduce several kinds of feature expressions of color, texture and shape. The proposed algorithm of the thesis uses the global and local spatial color histograms to describe the image color features. It is not only using simple calculation and fast retrieval advantage of the global color histograms, but including the image spatial distribution information in the local color histograms.
2、Research on wavelet transformation for extracting the image features. The wavelet transformation has the good partial analysis feature. This thesis introduces the method to extract the edge shape feature of the image object, which uses two-dimensional multi-scale wavelet analysis and the wavelet transform modulus maxima value and describes the image shape feature by the seven invariant moments.
3、Research on the image retrieval relevance feedback. Apply support vector machines (SVM) theory to image retrieval relevance feedback and discuss the influence of the choice of different kernel functions.
The experimental results show that the two-level image retrieval algorithm combined with the relevance feedback technology can get a better retrieval results.
引文
[1]章毓晋.基于内容的视觉信息检索[M]科学出版社.2002
[2]刘倩.基于内容的图像检索综述[J]现代计算机.2002.05 No.139.16-20
[3]杭燕,杨育彬,陈兆乾,基于内容的图像检索综述[J]计算机应用研究,2002.Vol 9.9-13,29
[4]http://wwwqbic.almaden.ibm.com/
[5]http://www.virage.com/home/index.en.html
[6]http://meru.rnet.missouri.edu/
[7]http://www.ctr.columbia.edu/
[8]Swain M, Ballard D. Color indexing[J]International Journal of Computer Vision.1991. Vol.7. No.l 11-32.
[9]Kam, A.H. Ng, T.T. Kingsbury, N.G. Fitzgerald, W.J. Content based image retrieval through object extraction and querying. [J] Proceedings of the IEEE Workshop on Content-Based Access of Image and Video Libraries. Hilton Head Island:IEEE Computer Society.2000.91-95.
[10]郭健,陈孝威.基于颜色分布的图像检索技术[J]计算机工程与应用 2006.6 45-47,74.
[ll]章毓晋.图象工程上册图像处理和分析[M]清华大学出版社.2004.
[12]Lenina BIRGALE, Manesh KOKARE and Dharmpal DOYE. Colour and Texture Features for Content Based Image Retrieval [J] IEEE Proceedings of the International Conference on Computer Graphics, Imaging and Visualisation (CGIV'06).2006.3.
[13]汪慧兰,赵海峰,罗斌.基于局部颜色空间特征的图像检索[J].计算机技术与发展.2006.1.Vol.16No.1.76-79.
[14]强振平,刘辉.局部累积直方图在彩色图像检索中的应用[J]计算机与数字工程.2006.Vol.34No.6.123-125.157
[15]Chao-bing Huang, Sheng-sheng Yu, Jing-li Zhou, Hang-wei Lu. Image Retrieval Using Both Color and Local Spatial Feature Histograms [J]IEEE 2004.7.927-931
[16]刘毅,张明.局部颜色特征在基于内容的图像检索中的应用[J]计算机应用,2004.7.Vol.24. No.7. 47-49
[17]JL Shih, LH Chen. Chen. Colour image retrieval based on primitives of colour moments[J] IEE proc.
Vis.Image Signal Process.2002.12.Vol.149. No.6.370-376
[18]http://www.csdn.net/
[19]吴成玉,邰晓英,赵杰煜.基于颜色特征的图像检索[J]计算机应用.2004.6.Vol.24.135-137
[20]黄诚,王国营.一种基于颜色聚合向量的图像检检索方法[J]计算机工程.2006.1.Vol.32. No.2.194-496.199
[21]Markus Stricker, Markus Orengo. Similarity of Color Images [J] Proc. of SPIE:Storage and Retrieval for Image and Video Databases,1995, Vol.2420.381-392
[22]R M Haralick, K Shanmugam, IH Dinstein. Textural features for image classification[J]IEEE Trans on Systems. Man, Cybernetics.1973, SMC-3(6):610-621.
[23]Manesh Kokare, B.N. Chatterji, P.K. Biswas.Cosine-modulated wavelet based texture features for content-based image retrieval[J] Pattern Recognition Letters 25.2004.391-398
[24]施智平,胡宏,李清勇,史忠植,段禅伦.基于纹理谱描述子的图像检索[J]软件学报.2005.Vol.16,No.6.1040-1045
[25]仇江涛,魏志强,谷鸣.基于纹理-形状-中心特征的图像检索[J]微计算机信息(测控自动化)2006Vol.22 No.6-1.235-237.
[26]丁险峰,吴洪,张宏江,马颂德.形状匹配综述[J]自动化学报.2001.9.Vol.127 No.15.678-694
[27]Dengsheng Zhang. Guojun Lu.Review of shape representation and description techniques[J] Pattern Recognition 37.2004.1-19
[28]董卫军.利用小波分析进行基于形状的图像检索技术研究[D].西北大学硕士论文,2001.
[29]刘寅,滕晓龙,刘重庆.复杂背景下基于傅立叶描述子的手势识别[J].计算机仿真.2005,12.Vol.22, No.10,158-161.
[30]王涛,刘文印,孙家广,张宏江.傅立叶描述子识别物体的形状[J]计算机研究与发展2002.12.Vol.39,No.12 1714-1719.
[31]刘广海.基于纹理和形状特征的图像检索技术研究[D].硕士论文.2004.
[32]黄勇,王崇骏,王亮.杭燕.陈兆乾.基于形状不变矩的图像检索算法的研究[J]计算机应用研究2004.Vol.7.256-257,260.
[33]陈武凡等.小波分析及其在图像处理中的应用[M]北京科技出版社,2002
[34]唐远炎,王玲.小波分析与文本文字识别[M]科学出版社.2004.5.
[35]Ingrid Duabechies.李建平,杨万年译.小波十讲[M]机械工业出版社,2002
[36][美]崔锦泰著,程正兴译.小波分析导论[M]西安交通大学出版社.1997.
[37]孙延奎.小波分析及其应用[M]机械工程出版社.2004.
[38]Canny J. A Computational Approach to Edge Detection [J] IEEE Trans. On PAMI,1986.Vol.8 No.6. 698-769
[39]薛鸿民,刘志镜,刘利,周生雷.基于形状的图像检索的关键技术研究[J].计算机应用研究.2002.Vol.11.61-64.
[40]黄元元.基于视觉特征的图像检索技术研究[D]南京理工大学博士论文.2003
[41]吴险峰.基于内容图像检索技术的研究和实现[D]四川大学工程硕士论文.2005.
[42]Stephane G. Mallat. Multiresolution Approximations and Wavelet Orthonormal Bases Of L2(R)[J] Transactions of the American Mathematical Society.1989,9.Vol.315. No.1.69-87
[43]Q.Tian, N.Sebe, M. S.Lew, E.Loupias, T.S.Huang.Content-Based Image Retrieval Using Wavelet-based Salient Points [J]Journal of Electronic Imaging.2001,10. Vol.10, No.4.835-849
[44]周敏,龙昭华.基于MAS小波变换的数字图像轮廓提取算法[J]重庆邮电学院学报.2004.4.Vol.16 No.2.44-48
[45]边肇祺,张学工等.模式识别(第二版)[M]清华大学出版社.2000.
[46]Nello Cristianini,JohnShawe-Taylor(著)李国正,王猛,曾华军(译).支持向量机导论(AnIntroduction to Support Vector Machines and Other Kernel-based Learning Methods) [M]电子工业出版社.2004.第一版.
[47]张学工.统计学习理论与支持向量机[J]自动化学报.2000.1,Vol.26.No.1
[48]Vapnik V N.The Nature of Statistical Learning Theory[M] New York:Springer-Verlag.1995
[49]田盛丰,黄厚宽.基于支持向量机的数据库学习算法[J]计算机研究与发展.2000.1.Vol.37,No.1.17-22
[50]肖靓.顾嗣扬.基于SVM综合利用颜色和纹理特征的图像分类和检索[J]通讯和计算机.2005,11.Vol.2, No.10 (Serial No.11),16-20.
[51]金磊.基于SVM的相关反馈图像检索技术的研究[D]华东师范大学 硕士论文.2006.
[52]李丽.基于SVM的人脸识别技术研究[D]中南大学硕十论文.2005.
[53]http://www.csie.ntu.edu.tw/-cjlin/libs4vm/
[54]HOI, Chu-Hong. Learning on Relevance Feedback in Content-based Image Retrieval[D] The Chinese University of Hong Kong.2004.7
[55]张芳,汪成军.基于支持向量机的手写体汉字的识别[J]计算机与数字工程.2006Vol.34. No.1.65-68.
[56]姚玉荣,章毓晋.利用小波和矩进行基于形状的图象检索[J]中国图象图形学报.2000.3.Vol.5(A),No.3.206-210
[57]吴成玉,邰晓英,赵杰煜.颜色、纹理、形状及相关反馈在图像检索中的应用[J]计算机工程与应用.2004.No.20.188-192
[58]http://bbs.matwav.com/
[59]E Osuna, R Freund, F Girosi. An Improved Training Algorithm for Support Vector Machines[J] Proceedings of IEEE Workshop on Neural Networks for Signal Processing.1997.9.276-285
[60]Vapnik V N. Estimation of Dependences Based on Empirical Data[M] Spring Verlag..1982
[2]刘倩.基于内容的图像检索综述[J]现代计算机.2002.05 No.139.16-20
[3]杭燕,杨育彬,陈兆乾,基于内容的图像检索综述[J]计算机应用研究,2002.Vol 9.9-13,29
[4]http://wwwqbic.almaden.ibm.com/
[5]http://www.virage.com/home/index.en.html
[6]http://meru.rnet.missouri.edu/
[7]http://www.ctr.columbia.edu/
[8]Swain M, Ballard D. Color indexing[J]International Journal of Computer Vision.1991. Vol.7. No.l 11-32.
[9]Kam, A.H. Ng, T.T. Kingsbury, N.G. Fitzgerald, W.J. Content based image retrieval through object extraction and querying. [J] Proceedings of the IEEE Workshop on Content-Based Access of Image and Video Libraries. Hilton Head Island:IEEE Computer Society.2000.91-95.
[10]郭健,陈孝威.基于颜色分布的图像检索技术[J]计算机工程与应用 2006.6 45-47,74.
[ll]章毓晋.图象工程上册图像处理和分析[M]清华大学出版社.2004.
[12]Lenina BIRGALE, Manesh KOKARE and Dharmpal DOYE. Colour and Texture Features for Content Based Image Retrieval [J] IEEE Proceedings of the International Conference on Computer Graphics, Imaging and Visualisation (CGIV'06).2006.3.
[13]汪慧兰,赵海峰,罗斌.基于局部颜色空间特征的图像检索[J].计算机技术与发展.2006.1.Vol.16No.1.76-79.
[14]强振平,刘辉.局部累积直方图在彩色图像检索中的应用[J]计算机与数字工程.2006.Vol.34No.6.123-125.157
[15]Chao-bing Huang, Sheng-sheng Yu, Jing-li Zhou, Hang-wei Lu. Image Retrieval Using Both Color and Local Spatial Feature Histograms [J]IEEE 2004.7.927-931
[16]刘毅,张明.局部颜色特征在基于内容的图像检索中的应用[J]计算机应用,2004.7.Vol.24. No.7. 47-49
[17]JL Shih, LH Chen. Chen. Colour image retrieval based on primitives of colour moments[J] IEE proc.
Vis.Image Signal Process.2002.12.Vol.149. No.6.370-376
[18]http://www.csdn.net/
[19]吴成玉,邰晓英,赵杰煜.基于颜色特征的图像检索[J]计算机应用.2004.6.Vol.24.135-137
[20]黄诚,王国营.一种基于颜色聚合向量的图像检检索方法[J]计算机工程.2006.1.Vol.32. No.2.194-496.199
[21]Markus Stricker, Markus Orengo. Similarity of Color Images [J] Proc. of SPIE:Storage and Retrieval for Image and Video Databases,1995, Vol.2420.381-392
[22]R M Haralick, K Shanmugam, IH Dinstein. Textural features for image classification[J]IEEE Trans on Systems. Man, Cybernetics.1973, SMC-3(6):610-621.
[23]Manesh Kokare, B.N. Chatterji, P.K. Biswas.Cosine-modulated wavelet based texture features for content-based image retrieval[J] Pattern Recognition Letters 25.2004.391-398
[24]施智平,胡宏,李清勇,史忠植,段禅伦.基于纹理谱描述子的图像检索[J]软件学报.2005.Vol.16,No.6.1040-1045
[25]仇江涛,魏志强,谷鸣.基于纹理-形状-中心特征的图像检索[J]微计算机信息(测控自动化)2006Vol.22 No.6-1.235-237.
[26]丁险峰,吴洪,张宏江,马颂德.形状匹配综述[J]自动化学报.2001.9.Vol.127 No.15.678-694
[27]Dengsheng Zhang. Guojun Lu.Review of shape representation and description techniques[J] Pattern Recognition 37.2004.1-19
[28]董卫军.利用小波分析进行基于形状的图像检索技术研究[D].西北大学硕士论文,2001.
[29]刘寅,滕晓龙,刘重庆.复杂背景下基于傅立叶描述子的手势识别[J].计算机仿真.2005,12.Vol.22, No.10,158-161.
[30]王涛,刘文印,孙家广,张宏江.傅立叶描述子识别物体的形状[J]计算机研究与发展2002.12.Vol.39,No.12 1714-1719.
[31]刘广海.基于纹理和形状特征的图像检索技术研究[D].硕士论文.2004.
[32]黄勇,王崇骏,王亮.杭燕.陈兆乾.基于形状不变矩的图像检索算法的研究[J]计算机应用研究2004.Vol.7.256-257,260.
[33]陈武凡等.小波分析及其在图像处理中的应用[M]北京科技出版社,2002
[34]唐远炎,王玲.小波分析与文本文字识别[M]科学出版社.2004.5.
[35]Ingrid Duabechies.李建平,杨万年译.小波十讲[M]机械工业出版社,2002
[36][美]崔锦泰著,程正兴译.小波分析导论[M]西安交通大学出版社.1997.
[37]孙延奎.小波分析及其应用[M]机械工程出版社.2004.
[38]Canny J. A Computational Approach to Edge Detection [J] IEEE Trans. On PAMI,1986.Vol.8 No.6. 698-769
[39]薛鸿民,刘志镜,刘利,周生雷.基于形状的图像检索的关键技术研究[J].计算机应用研究.2002.Vol.11.61-64.
[40]黄元元.基于视觉特征的图像检索技术研究[D]南京理工大学博士论文.2003
[41]吴险峰.基于内容图像检索技术的研究和实现[D]四川大学工程硕士论文.2005.
[42]Stephane G. Mallat. Multiresolution Approximations and Wavelet Orthonormal Bases Of L2(R)[J] Transactions of the American Mathematical Society.1989,9.Vol.315. No.1.69-87
[43]Q.Tian, N.Sebe, M. S.Lew, E.Loupias, T.S.Huang.Content-Based Image Retrieval Using Wavelet-based Salient Points [J]Journal of Electronic Imaging.2001,10. Vol.10, No.4.835-849
[44]周敏,龙昭华.基于MAS小波变换的数字图像轮廓提取算法[J]重庆邮电学院学报.2004.4.Vol.16 No.2.44-48
[45]边肇祺,张学工等.模式识别(第二版)[M]清华大学出版社.2000.
[46]Nello Cristianini,JohnShawe-Taylor(著)李国正,王猛,曾华军(译).支持向量机导论(AnIntroduction to Support Vector Machines and Other Kernel-based Learning Methods) [M]电子工业出版社.2004.第一版.
[47]张学工.统计学习理论与支持向量机[J]自动化学报.2000.1,Vol.26.No.1
[48]Vapnik V N.The Nature of Statistical Learning Theory[M] New York:Springer-Verlag.1995
[49]田盛丰,黄厚宽.基于支持向量机的数据库学习算法[J]计算机研究与发展.2000.1.Vol.37,No.1.17-22
[50]肖靓.顾嗣扬.基于SVM综合利用颜色和纹理特征的图像分类和检索[J]通讯和计算机.2005,11.Vol.2, No.10 (Serial No.11),16-20.
[51]金磊.基于SVM的相关反馈图像检索技术的研究[D]华东师范大学 硕士论文.2006.
[52]李丽.基于SVM的人脸识别技术研究[D]中南大学硕十论文.2005.
[53]http://www.csie.ntu.edu.tw/-cjlin/libs4vm/
[54]HOI, Chu-Hong. Learning on Relevance Feedback in Content-based Image Retrieval[D] The Chinese University of Hong Kong.2004.7
[55]张芳,汪成军.基于支持向量机的手写体汉字的识别[J]计算机与数字工程.2006Vol.34. No.1.65-68.
[56]姚玉荣,章毓晋.利用小波和矩进行基于形状的图象检索[J]中国图象图形学报.2000.3.Vol.5(A),No.3.206-210
[57]吴成玉,邰晓英,赵杰煜.颜色、纹理、形状及相关反馈在图像检索中的应用[J]计算机工程与应用.2004.No.20.188-192
[58]http://bbs.matwav.com/
[59]E Osuna, R Freund, F Girosi. An Improved Training Algorithm for Support Vector Machines[J] Proceedings of IEEE Workshop on Neural Networks for Signal Processing.1997.9.276-285
[60]Vapnik V N. Estimation of Dependences Based on Empirical Data[M] Spring Verlag..1982