基于颜色和纹理特征的图像检索研究
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摘要
基于内容的图像检索(CBIR)技术是当前研究的热点问题。它主要是利用图像的视觉特征，如图像的颜色、纹理、形状等特征来进行检索。它突破了传统的基于文本检索技术的局限，直接对图像内容进行分析并抽取特征，然后利用这些内容特征建立索引并进行检索。
本文针对基于内容的图像检索的主要方法进行了研究。在颜色特征提取和匹配中，通过研究和分析不同的颜色空间和颜色特征，提出对HSV颜色空间进行合理的量化，提取颜色直方图形成特征矢量用于检索的方法。针对颜色直方图不包含任何空间信息问题，本文给出了一种基于HSV色彩空间累加直方图进行图像检索的方法，并设计了一种具有重叠方式的图像分块方法，即合理地将图像分成若干子快，为图像提出了一定程度的位置信息。本文选取了基于量化的颜色特征、基于共生矩阵的纹理特征进行单特征检索，在这两个特征的基础上，通过构建各特征量的权值，来实现多特征的图像检索。
设计了分别基于颜色特征和基于纹理特征的两种图像检索算法。在利用单一特征检索的基础上，提出了一种综合利用上述两个特征共同进行检索的方法。对真实图像数据库的检索实验表明，综合特征检索要比单一特征检索更符合人的视觉感受要求，因而检索效果更好。
At present,Content-Based Image Retrieval(CBIR) is becoming a hot research topic.lt is a retrieval technology based on the vision features.such as the color.texture and shape of the image.lt is different from traditional retrieval technology based on the text.This technology directly analyzes image content and extracts image features. These contental features to be built index and be used in retrieval.
The main techniques of CBIR are discussed in this thesis. During the study of color features extraction and matching, HSV color model is chosen and divided into small spaces according to the perception of human eyes. The details of extracting HSV-based color histogram are described. In order to deal with the shortcoming of color histogram - it includes no spatial distribution information of the image, this thesis proposes a new content-based color image retrieval method, in which both the color and the spatial relationship of image are taken into account and then designs 12 overlapped sub-regions to get the color accumulative histogram in each region.The new method divides the image into blocks, which contain spatial information. The color feature based quantification,texture feature based Co-occurrence matrix are selected in feature retrieval.In multi-features retrieval,the feedback will be used in adjusting weight and re-used in multi-features retrieval.
The methods for image retrieval using color and texture features are first discussed. On the basis of using color and texture features separately, a new method for image retrieval using combined color and texture feature is proposed. Retrienal experiments using real color image database are carried out.The results show that the retrieval results obtained from combined-features fits more closely with human perception than the retrieval results obtained from single-features.
本文针对基于内容的图像检索的主要方法进行了研究。在颜色特征提取和匹配中，通过研究和分析不同的颜色空间和颜色特征，提出对HSV颜色空间进行合理的量化，提取颜色直方图形成特征矢量用于检索的方法。针对颜色直方图不包含任何空间信息问题，本文给出了一种基于HSV色彩空间累加直方图进行图像检索的方法，并设计了一种具有重叠方式的图像分块方法，即合理地将图像分成若干子快，为图像提出了一定程度的位置信息。本文选取了基于量化的颜色特征、基于共生矩阵的纹理特征进行单特征检索，在这两个特征的基础上，通过构建各特征量的权值，来实现多特征的图像检索。
设计了分别基于颜色特征和基于纹理特征的两种图像检索算法。在利用单一特征检索的基础上，提出了一种综合利用上述两个特征共同进行检索的方法。对真实图像数据库的检索实验表明，综合特征检索要比单一特征检索更符合人的视觉感受要求，因而检索效果更好。
At present,Content-Based Image Retrieval(CBIR) is becoming a hot research topic.lt is a retrieval technology based on the vision features.such as the color.texture and shape of the image.lt is different from traditional retrieval technology based on the text.This technology directly analyzes image content and extracts image features. These contental features to be built index and be used in retrieval.
The main techniques of CBIR are discussed in this thesis. During the study of color features extraction and matching, HSV color model is chosen and divided into small spaces according to the perception of human eyes. The details of extracting HSV-based color histogram are described. In order to deal with the shortcoming of color histogram - it includes no spatial distribution information of the image, this thesis proposes a new content-based color image retrieval method, in which both the color and the spatial relationship of image are taken into account and then designs 12 overlapped sub-regions to get the color accumulative histogram in each region.The new method divides the image into blocks, which contain spatial information. The color feature based quantification,texture feature based Co-occurrence matrix are selected in feature retrieval.In multi-features retrieval,the feedback will be used in adjusting weight and re-used in multi-features retrieval.
The methods for image retrieval using color and texture features are first discussed. On the basis of using color and texture features separately, a new method for image retrieval using combined color and texture feature is proposed. Retrienal experiments using real color image database are carried out.The results show that the retrieval results obtained from combined-features fits more closely with human perception than the retrieval results obtained from single-features.
引文
[1] 刘士林．基于内容的图象检索．佳木斯大学学报(自然科学版)，2001年9月第19卷第3期，PP．257-259
[2] 庄越挺，潘云鹤，吴飞编著．网上多媒体信息分析与检索：清华大学出版社，2001．12
[3] Niblack, et al. "The QBIC Project: Querying Images By Content Using Color, Texture, and Shape", Pro. of SPIE, Storage and Retrieval for Image and Video Databases, February 1993.
[4] M. Flicker. et al., "Query by Image and video Content: The QBIC System", IEEE Computers, 1995
[5] IBM Corporations, "Query By Image Content(QBIC) Programmer's Guide", IBM Corporation, 1998
[6] The Virage Home Page:http://www.virage.com/
[7] A. Pentland. R. W. Picard and S. Sclaroff. "Photobook: Content-based Manipulation of Image Databases", Int Journal of Computer Vision. 1996
[8] John R. Smith and Shih-Fu Chang, "VisualSEEK: A Fully Automated Content-based Image Query System," ACM Multimedia 96, Boston, MA, NOV. 1996
[9] John R. Smith and Shih-Fu Chang, "Visually Searching the Web for Content", IEEE Multimedia magazine, Summer. 1997.
[10] Yong Rui, Thomas S. Huang and Sharad Mehrotra, "Conted-based Image Retrieval With Relevance Feedback In Mars", IEEE, 1997.
[11] Thomas. S. Huang. S. Mehrotra and K. Pamchandran, "Multimedia Analysis and Retrieval System(MARS) Project". Proc of 33nd Annual Clinic on Library Application of Data processing-Digital Image Access and Retrieval. March 1996
[12] 罗睿，张永生，范永弘．遥感图像数据库基于内容查询的研究．遥感学报，2002年1月第6卷第1期，pp．24-29
[13] 巩志国等．领域相关多媒体对象的基于内容查询．计算机学报，2002年，pp．64-72
[14] 庄越挺，潘云鹤．基于内容的图像检索综述．模式识别与人工智能，1999年6月第12卷第2期，pp．170-177
[15] 陈纯等．《计算机图像处理技术与算法》．清华大学出版社，2003(1)125．89
[16] 胡晓峰，刘毅．QBIC：一个典型的基于内容的检索系统．微型计算机，1996年第16卷第6期，pp．4-7
[17] 李向阳，鲁东明，潘云鹤．基于色彩的图像数据库检索方法的研究．计算机研究与发展，1999，3．36(3)．
[18] 李国辉，柳伟，曹莉华．一种基于颜色特征的图象检索方法．中国图象图形学报，1999年3月第4卷(A版)第3期，pp．248-251
[19] 刘忠伟，章毓晋．利用颜色特征进行图像检索．电子技术应用，25(2)：19-20．1999．
[20] Stricker M A, Orengo M. Similarity of color images[A]. in: Proc. of SPIE[C]: Storage and Retrieval for Image and Video Databases Ⅲ, Feb. 1995, 2185: 381-392.
[21] Smith J R, Chang S F. Tools and techniques for color image retrieval [A]. in: Proc. of SPIE: Storage and Retrieval for Image and Video Database [C], San Jose CA, 1995, 2670: 426-437.
[22] Pass G, Zabih R. Histogram refinement for content-base image retrieval [A]. in: IEEE Workshop on Applications of Computer Vision [C], 1996. 96-102.
[23] White D, Jain R. Algorithm and strategies for similarity retrieval. San Diego: In TR VCL-96-101 University of California, 1996.
[24] M Das, E M riseman, B A Draper. FOCUS: searching for multi-color objects in a diverse image database [A]. In Proceeding IEEE Computer Society Conference on Computer Vision and Pattern Recognition [C]. Puerto Rico, San Juan Puerto Rico: IEEE, 1997. pp. 756-761.
[25] Y Liu, S Ozawa. An integrated color-spatial image representation and the similar image retrieval[A]. Proceedings of 4th Southwest Symposium on Image Analysis and Interpretation[C]. Texas, Austin, Texas: IEEE, 2000. pp. 283-287.
[26] J Smith, S F Chang. Tools and techniques for color image retrieval[A]. In SPIE Proceeding[C]. Bellingham, Washington: SPIE, 1996. pp. 1630-1639.
[27] I K Park, I D Yun, S U Lee. Models and algorithms for efficient color image indexing[A]. Proceedings IEEE Workshop in Content-Based Access of Image and Video Libraries [C]. Puerto Rico, Puerto Rico: IEEE, 1997. pp. 36-41.
[28] 何清法，李国杰．综合分块主色和相关反馈技术的图像检索方法．计算机辅助设计与图形学学报，2001，10．13(6)：912-917
[29] 容观澳．计算机图像处理．清华大学出版社，2000．pp．130-131．
[30] Jia Kebin, Fang Sheng, Zhu Qing. Rotation and Translation Invariant Color Image Retrieval. ICSP'02 Proceedings. pp. 1063-1066. 2002.
[31] B S Manjunath, W Y Ma. Texture feature for browsing and retrieval of image data[J]. IEEE Transaction on PAMI, 1996, 18(8): 837-842.
[32] 刘忠伟，章毓晋．综合利用颜色和纹理特征的图像检索．通信学报，1999年5月第20卷第5期，PP．36-40
[33] 夏良正．数字图像处理．东南大学出版社，1999．
[34] 刘鹏宇．基于内容的图像特征提取算法的研究．吉林大学硕士学位论文，pp．30，2004．
[35] 霍宏涛．数字图像处理．机械工业出版社，2003．
[36] Ma W Y, Manjunath B S. A comparison of wavelet features for texture annotation[A]. in: Proc of IEEE Int Conf on Image Processing[C], Washington DC, Oct. 1995, 2(4): 256-259.
[37] 方昭辉．基于内容的图像检索中索引的研究与实现．南京师范大学硕士学位论文，pp．14，2004．
[38] 董卫军．基于小波变换的图像图像处理技术研究．西北工业大学博士学位论文．pp．16，2006．
[39] 王惠明，史萍．图像纹理特征的提取方法．中国传媒大学学报自然科学版，2006年3月第13卷第1期，pp．49-52
[40] 刘鹏宇．基于内容的图像特征提取算法的研究．吉林大学硕士学位论文，pp．30，2004．
[41] Ortega M. Supporting Similarity Queries in MARS. In: ACM Confon. Multimedia, 1997: 403-413
[42] 陈兵奇，孙明．Visual C++实用图像处理专业教程．清华大学出版社， 2004．
[43] 王洪涛．深入剖析Visual C++编程技术及应用实例．人民邮电出版社，2003
[44] 刘斌，王忠．面向对象程序设计：Visual C++．清华大学出版社，2003
[45] 许强．Access 2000数据库设计管理与应用．国防工业出版社，2002
[46] 刘娜，张海成，刘智渊，彭晓明．灰度共生矩阵与Sobel算子结合的图像检索方法．空军雷达学院学报．2006年3月第20卷第2期，pp．126-128
[47] 董卫军，周明全，耿国华．基于纹理-形状特征的图像检索技术．计算机工程与应用，2004年24期．pp．9-14．
[48] 孟丽娜．基于区域的图像检索技术研究．西安电子科技硕士学位论文，pp．11，2006．
[49] MontielE. TextureClassificationviaConditionalHistogram[J]. PatternRecog nitionLetters, 2005, 26(11): 174021751.
[50] 马颂德，张正友．计算机视觉[M]．北京：科学出版社，2003．
[51] 姚华静．基于颜色空间和纹理特征的图像检索．中国科技论文在线．http://www.paper.edu.cn
[2] 庄越挺，潘云鹤，吴飞编著．网上多媒体信息分析与检索：清华大学出版社，2001．12
[3] Niblack, et al. "The QBIC Project: Querying Images By Content Using Color, Texture, and Shape", Pro. of SPIE, Storage and Retrieval for Image and Video Databases, February 1993.
[4] M. Flicker. et al., "Query by Image and video Content: The QBIC System", IEEE Computers, 1995
[5] IBM Corporations, "Query By Image Content(QBIC) Programmer's Guide", IBM Corporation, 1998
[6] The Virage Home Page:http://www.virage.com/
[7] A. Pentland. R. W. Picard and S. Sclaroff. "Photobook: Content-based Manipulation of Image Databases", Int Journal of Computer Vision. 1996
[8] John R. Smith and Shih-Fu Chang, "VisualSEEK: A Fully Automated Content-based Image Query System," ACM Multimedia 96, Boston, MA, NOV. 1996
[9] John R. Smith and Shih-Fu Chang, "Visually Searching the Web for Content", IEEE Multimedia magazine, Summer. 1997.
[10] Yong Rui, Thomas S. Huang and Sharad Mehrotra, "Conted-based Image Retrieval With Relevance Feedback In Mars", IEEE, 1997.
[11] Thomas. S. Huang. S. Mehrotra and K. Pamchandran, "Multimedia Analysis and Retrieval System(MARS) Project". Proc of 33nd Annual Clinic on Library Application of Data processing-Digital Image Access and Retrieval. March 1996
[12] 罗睿，张永生，范永弘．遥感图像数据库基于内容查询的研究．遥感学报，2002年1月第6卷第1期，pp．24-29
[13] 巩志国等．领域相关多媒体对象的基于内容查询．计算机学报，2002年，pp．64-72
[14] 庄越挺，潘云鹤．基于内容的图像检索综述．模式识别与人工智能，1999年6月第12卷第2期，pp．170-177
[15] 陈纯等．《计算机图像处理技术与算法》．清华大学出版社，2003(1)125．89
[16] 胡晓峰，刘毅．QBIC：一个典型的基于内容的检索系统．微型计算机，1996年第16卷第6期，pp．4-7
[17] 李向阳，鲁东明，潘云鹤．基于色彩的图像数据库检索方法的研究．计算机研究与发展，1999，3．36(3)．
[18] 李国辉，柳伟，曹莉华．一种基于颜色特征的图象检索方法．中国图象图形学报，1999年3月第4卷(A版)第3期，pp．248-251
[19] 刘忠伟，章毓晋．利用颜色特征进行图像检索．电子技术应用，25(2)：19-20．1999．
[20] Stricker M A, Orengo M. Similarity of color images[A]. in: Proc. of SPIE[C]: Storage and Retrieval for Image and Video Databases Ⅲ, Feb. 1995, 2185: 381-392.
[21] Smith J R, Chang S F. Tools and techniques for color image retrieval [A]. in: Proc. of SPIE: Storage and Retrieval for Image and Video Database [C], San Jose CA, 1995, 2670: 426-437.
[22] Pass G, Zabih R. Histogram refinement for content-base image retrieval [A]. in: IEEE Workshop on Applications of Computer Vision [C], 1996. 96-102.
[23] White D, Jain R. Algorithm and strategies for similarity retrieval. San Diego: In TR VCL-96-101 University of California, 1996.
[24] M Das, E M riseman, B A Draper. FOCUS: searching for multi-color objects in a diverse image database [A]. In Proceeding IEEE Computer Society Conference on Computer Vision and Pattern Recognition [C]. Puerto Rico, San Juan Puerto Rico: IEEE, 1997. pp. 756-761.
[25] Y Liu, S Ozawa. An integrated color-spatial image representation and the similar image retrieval[A]. Proceedings of 4th Southwest Symposium on Image Analysis and Interpretation[C]. Texas, Austin, Texas: IEEE, 2000. pp. 283-287.
[26] J Smith, S F Chang. Tools and techniques for color image retrieval[A]. In SPIE Proceeding[C]. Bellingham, Washington: SPIE, 1996. pp. 1630-1639.
[27] I K Park, I D Yun, S U Lee. Models and algorithms for efficient color image indexing[A]. Proceedings IEEE Workshop in Content-Based Access of Image and Video Libraries [C]. Puerto Rico, Puerto Rico: IEEE, 1997. pp. 36-41.
[28] 何清法，李国杰．综合分块主色和相关反馈技术的图像检索方法．计算机辅助设计与图形学学报，2001，10．13(6)：912-917
[29] 容观澳．计算机图像处理．清华大学出版社，2000．pp．130-131．
[30] Jia Kebin, Fang Sheng, Zhu Qing. Rotation and Translation Invariant Color Image Retrieval. ICSP'02 Proceedings. pp. 1063-1066. 2002.
[31] B S Manjunath, W Y Ma. Texture feature for browsing and retrieval of image data[J]. IEEE Transaction on PAMI, 1996, 18(8): 837-842.
[32] 刘忠伟，章毓晋．综合利用颜色和纹理特征的图像检索．通信学报，1999年5月第20卷第5期，PP．36-40
[33] 夏良正．数字图像处理．东南大学出版社，1999．
[34] 刘鹏宇．基于内容的图像特征提取算法的研究．吉林大学硕士学位论文，pp．30，2004．
[35] 霍宏涛．数字图像处理．机械工业出版社，2003．
[36] Ma W Y, Manjunath B S. A comparison of wavelet features for texture annotation[A]. in: Proc of IEEE Int Conf on Image Processing[C], Washington DC, Oct. 1995, 2(4): 256-259.
[37] 方昭辉．基于内容的图像检索中索引的研究与实现．南京师范大学硕士学位论文，pp．14，2004．
[38] 董卫军．基于小波变换的图像图像处理技术研究．西北工业大学博士学位论文．pp．16，2006．
[39] 王惠明，史萍．图像纹理特征的提取方法．中国传媒大学学报自然科学版，2006年3月第13卷第1期，pp．49-52
[40] 刘鹏宇．基于内容的图像特征提取算法的研究．吉林大学硕士学位论文，pp．30，2004．
[41] Ortega M. Supporting Similarity Queries in MARS. In: ACM Confon. Multimedia, 1997: 403-413
[42] 陈兵奇，孙明．Visual C++实用图像处理专业教程．清华大学出版社， 2004．
[43] 王洪涛．深入剖析Visual C++编程技术及应用实例．人民邮电出版社，2003
[44] 刘斌，王忠．面向对象程序设计：Visual C++．清华大学出版社，2003
[45] 许强．Access 2000数据库设计管理与应用．国防工业出版社，2002
[46] 刘娜，张海成，刘智渊，彭晓明．灰度共生矩阵与Sobel算子结合的图像检索方法．空军雷达学院学报．2006年3月第20卷第2期，pp．126-128
[47] 董卫军，周明全，耿国华．基于纹理-形状特征的图像检索技术．计算机工程与应用，2004年24期．pp．9-14．
[48] 孟丽娜．基于区域的图像检索技术研究．西安电子科技硕士学位论文，pp．11，2006．
[49] MontielE. TextureClassificationviaConditionalHistogram[J]. PatternRecog nitionLetters, 2005, 26(11): 174021751.
[50] 马颂德，张正友．计算机视觉[M]．北京：科学出版社，2003．
[51] 姚华静．基于颜色空间和纹理特征的图像检索．中国科技论文在线．http://www.paper.edu.cn
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