基于颜色、纹理的图像检索
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摘要
随着多媒体及通信技术的快速发展，数字图像已涉及到人们生活的各个领域，成为多媒体信息的一个重要组成部分。而随着网络技术的发展以及图像采集设备的日益普及，数字图像的种类和数量也在不断扩大，如何快速准确的找到用户所需要的图像资源，成为信息检索的一个重要方面，也成为人们研究的热点。基于内容的图像检索受到了广泛的关注，其主要是利用图像的颜色、纹理、形状、空间关系以及语义等客观特征进行检索。
颜色、纹理和形状做为图像重要的视觉特征，与图像内容之间有着密切的联系，图像这些特征提取的好坏，直接影响着图像检索的结果。因此，本文基于这些特征，对图像检索做了以下两方面的工作。
1.提出了一种基于边缘加细直方图的图像检索算法。颜色做为图像视觉重要的感知特征，对其提取通常会出现丢失空间信息或不能充分体现空间信息的情况。针对此缺点，本文根据图像不同位置具有不同重要性的特征提出了一种新的对图像进行“回”字形区域的划分，并结合塔式梯度直方图(PHOG),提出一种加细边缘直方图的图像检索算法。实验表明，该算法能较准确和高效的查找出用户所需要的彩色图像，且有较高的查准率和查全率。
2.提出了一种基于纹理基元融合直方图的图像检索算法。自然图像包含着丰富的颜色和纹理信息,从微观结构上说，颜色和纹理之间有着密切的关系。本文通过利用纹理基元思想定义的四种纹理基元模板，提取彩色图像的纹理基元图像，不仅充分体现了图像的颜色和纹理信息，还充分反映了颜色的空间位置，避免了对颜色空间信息丢失的缺点，并结合图像的梯度角，构造一种融合直方图，用于图像检索。实验结果表明，本文算法能更优先的检索出相似图像，具有较高的查全率和查准率。
With the multimedia and communication technology rapid developed, digital image has been involvedin all areas of people's lives, and becomes an important component in multimedia information. Along withthe development of network technology and the increasing popularity of image collection equipment, thetype of digital images is expanding continually, and how to find the user needs images quickly andaccurately to become an important aspect of information retrieval. Content-based image retrieval has beenwidespread concern, its basically is to use the color, texture, shape, spatial relationships and semanticcharacteristics to image retrieval. The quality of these features extraction has a direct impact on the resultsof image retrieval, so this article based on the characteristics of image retrieval do the following twoaspects.
1. This paper proposed a image retrieval algorithms based on the refined edge histogram. Color is animportant visual feature of images. However, when extracting color features it will loss spatial informationand lead to false retrieval. In this paper, we present a "Back"-shape regional division approach and combinewith pyramid histogram of orientated gradients (PHOG) to extract image edge features, termed refinededge histogram. Moreover, the refined edge histogram is applied to color image retrieval. Experimentalresults show that the proposed EDH are suitable for color image retrieval and has higher precision andrecall compared to other existing methods.
2. This paper proposed a image retrieval algorithms based on the texton fusion histogram. Naturalimages include rich color and texture informations, from the micro-structure that there is a closerelationship between color and texture. In this paper, we use four texton templates to extract the texton image of the color image. It is not only reflects the color and texture information fully, but also avoid theloss of color space information, and combined with the gradient angle of the image, construct a fusionhistogram for image retrieval. Experiments shows that this method retrieved similar images more priorityand has higher precision and recall than single features and traditional composite character.
颜色、纹理和形状做为图像重要的视觉特征，与图像内容之间有着密切的联系，图像这些特征提取的好坏，直接影响着图像检索的结果。因此，本文基于这些特征，对图像检索做了以下两方面的工作。
1.提出了一种基于边缘加细直方图的图像检索算法。颜色做为图像视觉重要的感知特征，对其提取通常会出现丢失空间信息或不能充分体现空间信息的情况。针对此缺点，本文根据图像不同位置具有不同重要性的特征提出了一种新的对图像进行“回”字形区域的划分，并结合塔式梯度直方图(PHOG),提出一种加细边缘直方图的图像检索算法。实验表明，该算法能较准确和高效的查找出用户所需要的彩色图像，且有较高的查准率和查全率。
2.提出了一种基于纹理基元融合直方图的图像检索算法。自然图像包含着丰富的颜色和纹理信息,从微观结构上说，颜色和纹理之间有着密切的关系。本文通过利用纹理基元思想定义的四种纹理基元模板，提取彩色图像的纹理基元图像，不仅充分体现了图像的颜色和纹理信息，还充分反映了颜色的空间位置，避免了对颜色空间信息丢失的缺点，并结合图像的梯度角，构造一种融合直方图，用于图像检索。实验结果表明，本文算法能更优先的检索出相似图像，具有较高的查全率和查准率。
With the multimedia and communication technology rapid developed, digital image has been involvedin all areas of people's lives, and becomes an important component in multimedia information. Along withthe development of network technology and the increasing popularity of image collection equipment, thetype of digital images is expanding continually, and how to find the user needs images quickly andaccurately to become an important aspect of information retrieval. Content-based image retrieval has beenwidespread concern, its basically is to use the color, texture, shape, spatial relationships and semanticcharacteristics to image retrieval. The quality of these features extraction has a direct impact on the resultsof image retrieval, so this article based on the characteristics of image retrieval do the following twoaspects.
1. This paper proposed a image retrieval algorithms based on the refined edge histogram. Color is animportant visual feature of images. However, when extracting color features it will loss spatial informationand lead to false retrieval. In this paper, we present a "Back"-shape regional division approach and combinewith pyramid histogram of orientated gradients (PHOG) to extract image edge features, termed refinededge histogram. Moreover, the refined edge histogram is applied to color image retrieval. Experimentalresults show that the proposed EDH are suitable for color image retrieval and has higher precision andrecall compared to other existing methods.
2. This paper proposed a image retrieval algorithms based on the texton fusion histogram. Naturalimages include rich color and texture informations, from the micro-structure that there is a closerelationship between color and texture. In this paper, we use four texton templates to extract the texton image of the color image. It is not only reflects the color and texture information fully, but also avoid theloss of color space information, and combined with the gradient angle of the image, construct a fusionhistogram for image retrieval. Experiments shows that this method retrieved similar images more priorityand has higher precision and recall than single features and traditional composite character.
引文
[1] S. K. Chang, C. W. Yan, D. C. Dimitroff. An intelligent image database system.IEEE Trans. on Software Eng.1988,14(5):412-421.
[2]李向阳，庄越挺，潘云鹤.基于内容的图像检索技术与系统.计算机科学，2001,38（3）：344-354.
[3]孙君顶.基于内容的图像检索技术研究.博士论文，西安电子科技大学，2005.
[4] A. Berman, L. Shapiro. Efficient image retrieval with multiple distance me assures.Spie3022:12-21.
[5] M. J. Swain, D. H. Ballard. Color indexing. International Journal of Computer Vision.1991,7(1):11-32.
[6] M. Stricker, M.Orengo. Similarity of color images. Storage and Retrieval for Image andVideo Database.1995.2(4):381-392.
[7] G. Pass, R. Zabin, J. Mill. Comparing images using color coherence vectors.4th ACMConf on Multimedia, Boston, Massachusetts, USA.1996,(11):65-73.
[8] J. Huang. Color-Spatial Image Indexing and Appliations. Phd thesis Cornell University,New York, USA.
[9] A. Rao, R. Srihari, Z. Zhang. Spatial Color Histogram for Content-Based Retrieval. IEEEInternational Conference on Tools with AI.1999:183-186.
[10] L. Cinque, S. Levialdi, K. Olsen, et al. Color-based image retrieval usingspatial-chromatic histograms. In Muitimedia Computing and System, IEEE Intl Conf.1999:969-973.
[11] S. Lim, G. J. Lu. Spatial statistics for content-based image retrieval. ITCC.2003.
[12] R. M. Haralick, K. Shanmugam. Texture features for iamge classification. On system,man and cybernetics, SMC-3(6):610-621.
[13] H. Tumara, S. Mori, T. Yamawaki. Texture features corresponding to visual perception.IEEE-SMC.1978,8(6):460-473.
[14] T. Ojala, M. Pietikainen, D. Harwood. A comparative study od texture measures withclassification based on featured distribution. Pattern Recognition.1996,29(1):51-59.
[15] D. S. Zhang. Image retrieval based on shape. Phd Thesis, Monash University.2002.
[16] P. Minghong, M. K. Mandal, A. Basu. Image retrieval based on histogram of fractalparameters. IEEE Transactions on Multimedia.2005,7(4):597-605.
[17] I. Kunttu, L. Lepisto, J. Rauhamaa, A. Visa. Multiscale fourier descriptors for defectimage retrieval. PRI.2006,27(2):123-132.
[18] M.Flickner, H. Sawhney, W. Niblack, et al. Query by image and video content: the QBICSystem. IEEE Computer.1995,28(9):23-32.
[19] J. R. Bach, C. Fuller, A. Gupta, A. Hampapur, B. Horowitz, R. Humphrey, R. C. Jain,C. F. Shu. The virage image search engine: An open framework for image management. SPIEConference on Storage and Retrieval for Still Image and Video Databases.1996,2670:76-87.
[20] J. Dowe. Content-based Retrieval in Multimedia Imaging. Proc SPIE Storage and Retrievalfor Image and Video Databases.1993.
[21] A. Pentland, R. W. Picard, S. Sclaroff. Photobook: Content-based manipulation of iamgedatabases. International Journal of Computer Vision.1996,18(3):233-254.
[22] J. R. Smith, S. F. Chang. VisualSEEK: a fully automated content-based image query system.The4th ACM international conference on Multimedia.1997:87-98.
[23] Y. Ma, B. S. Manjunath. Netra: A toolbox for Navigating Large Image Database. ProcIEEE Int. Conf. On Image Processing.
[24] R. Datta,D. Jnshi, J. Li, et al. Image Retrieval: Ideas, Influences, and Trends ofthe New Age. ACM Computing Surveys.2008,40(2):l一60.
[25] Yoo Hun-Woo, Jung She-Hwan. Extraction of Major Object Feature Using VQ Clusteringfor Content-based Image Retrieval. Pattern Recognition.2002,35(2):1115-1126.
[26] Lin Hwei-Jen, Kao Yang-Ta. A Study of Shape-based Image Retrieval. IEEE Computer.2004,22(2):645-662.
[27] Yu Honpug-Heather. Visual Image Retrieval on Compressed Domain with Q-Distance ImageSignal Process. International Journal of Computer Vision.2000,31(1):134-149.
[28] M. J. Swain, D. H. Ballard. Color Indexing.International Journal of ComputerVision.1991,7(1):11-32.
[29] E. Montiel. Texture Classification via Conditional Histograms. Pattern RecognitionLetters.2005,26(11):1740-1751.
[30]林克正,张彩华,刘丕娥.基于分块主颜色匹配的图像检索.计算机工程.2010,36(13):186-188.
[31] Zhans Dengsheng, Lu Guojun. Review of Shape Representation and Description Techniques.Pattem Recognition.2004,37(1):1-19.
[32] X. Q. Xu, D. J. Lee, S. Antani, et al. A Spine X-Bay Image Retrieval System UsingPartial Shape Matching. IEEE Trans on Information Technology in Biomedicine.2008,12(1):100-108.
[33] C. H. Wei, Y. Li, W. Y. Chau, et al. Trademark Image Retrieval Using Synthetic Featuresfor Describing Global Shape and Interior Structure. Pattem Recognition.2009,42(3):386—394.
[34]王向阳,陈景伟,于永健.一种基于彩色边缘综合特征的图像检索算法.模式识别与人工智能.2010,23(2):216-220.
[35] K. R. Castleman.数字图像处理.北京:电子工业出版社,1998.
[36]周明全,耿国华,韦娜.基于内容图像检索技术.北京：清华大学出版社，2007:22.
[37] N. Dalal, B. Triggs. Histograms of oriented gradients for human detection. Proceedingsof the IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR'05), San Diego, CA, USA,2005, Vol1:886-893.
[38] A. Bosch, A. Zisserman, X. Munoz. Representing shape with a spatial pyramid kernel.Proceedings of the6th ACM international conference on Image and video retrieval,Amsterdam, Dutch.2007:401-40
[39] Y. Rui, T. S. Huang, S. F. Chang. Image Retrieval: Current Techniques, PromisingDirections and Open Issues. Journal of Visual Communication and Image Representation.1999,10(4):39-62.
[40] M. Koskela, J. Laaksonen, Oja E. Comparison of Techniques for Content-based ImageRetrieval. Scandinavian Conference on Image Analysis, Bergen.2001:579-586.
[41] B. S. Manjunath, W. Y. Ma. Texture Features for Browsing and Retrieval of Image Data.IEEE Transactions on Pattern Analysis and Machine Intelligence.1996,18(8):837-842.
[42] B. Julesz. Textons: the elements of texture perception and their interactions. Nature290(5802).1981:91–97.
[43] B. Julesz. Texton gradients: the texton theory revisited. Biological Cybernetics.1986:245–251.
[44]魏宝刚，李向阳，鲁东明等.彩色分割研究进展.计算机科学.1999,26(4):59-62.
[45] F. G. Meyer, R. R. Coifman. Brushlets: A tool for directional image analysis and imagecompression. J. Of ACHA.1997,(5):147-187.
[46]章毓晋.图像工程上册-图像处理与分析.北京:清华大学出版社，1999.
[47] J. Beck. Effect of orientation and of shape similarity on perceptual grouping.Perceptual paychophysics.1(7):300-302.
[48] J. R. Bergen, B. Julesz. Parallel versus serial processing in pattern discrimination.Natural.1983,303(7):696-698.
[49] J. R. Bergen, E. H. Adelson. Early vision and texture perception using featuredistribution. Pattern Recognition.1999,32(3):447-486.
[50] G. H. Liu, L. Zhang, Y. K. Hou, Z. Y. Li, J. Y. Yang. Image retrieval based onmulti-texton histogram. Pattern Recognition.2010,43:2380-2389.
[2]李向阳，庄越挺，潘云鹤.基于内容的图像检索技术与系统.计算机科学，2001,38（3）：344-354.
[3]孙君顶.基于内容的图像检索技术研究.博士论文，西安电子科技大学，2005.
[4] A. Berman, L. Shapiro. Efficient image retrieval with multiple distance me assures.Spie3022:12-21.
[5] M. J. Swain, D. H. Ballard. Color indexing. International Journal of Computer Vision.1991,7(1):11-32.
[6] M. Stricker, M.Orengo. Similarity of color images. Storage and Retrieval for Image andVideo Database.1995.2(4):381-392.
[7] G. Pass, R. Zabin, J. Mill. Comparing images using color coherence vectors.4th ACMConf on Multimedia, Boston, Massachusetts, USA.1996,(11):65-73.
[8] J. Huang. Color-Spatial Image Indexing and Appliations. Phd thesis Cornell University,New York, USA.
[9] A. Rao, R. Srihari, Z. Zhang. Spatial Color Histogram for Content-Based Retrieval. IEEEInternational Conference on Tools with AI.1999:183-186.
[10] L. Cinque, S. Levialdi, K. Olsen, et al. Color-based image retrieval usingspatial-chromatic histograms. In Muitimedia Computing and System, IEEE Intl Conf.1999:969-973.
[11] S. Lim, G. J. Lu. Spatial statistics for content-based image retrieval. ITCC.2003.
[12] R. M. Haralick, K. Shanmugam. Texture features for iamge classification. On system,man and cybernetics, SMC-3(6):610-621.
[13] H. Tumara, S. Mori, T. Yamawaki. Texture features corresponding to visual perception.IEEE-SMC.1978,8(6):460-473.
[14] T. Ojala, M. Pietikainen, D. Harwood. A comparative study od texture measures withclassification based on featured distribution. Pattern Recognition.1996,29(1):51-59.
[15] D. S. Zhang. Image retrieval based on shape. Phd Thesis, Monash University.2002.
[16] P. Minghong, M. K. Mandal, A. Basu. Image retrieval based on histogram of fractalparameters. IEEE Transactions on Multimedia.2005,7(4):597-605.
[17] I. Kunttu, L. Lepisto, J. Rauhamaa, A. Visa. Multiscale fourier descriptors for defectimage retrieval. PRI.2006,27(2):123-132.
[18] M.Flickner, H. Sawhney, W. Niblack, et al. Query by image and video content: the QBICSystem. IEEE Computer.1995,28(9):23-32.
[19] J. R. Bach, C. Fuller, A. Gupta, A. Hampapur, B. Horowitz, R. Humphrey, R. C. Jain,C. F. Shu. The virage image search engine: An open framework for image management. SPIEConference on Storage and Retrieval for Still Image and Video Databases.1996,2670:76-87.
[20] J. Dowe. Content-based Retrieval in Multimedia Imaging. Proc SPIE Storage and Retrievalfor Image and Video Databases.1993.
[21] A. Pentland, R. W. Picard, S. Sclaroff. Photobook: Content-based manipulation of iamgedatabases. International Journal of Computer Vision.1996,18(3):233-254.
[22] J. R. Smith, S. F. Chang. VisualSEEK: a fully automated content-based image query system.The4th ACM international conference on Multimedia.1997:87-98.
[23] Y. Ma, B. S. Manjunath. Netra: A toolbox for Navigating Large Image Database. ProcIEEE Int. Conf. On Image Processing.
[24] R. Datta,D. Jnshi, J. Li, et al. Image Retrieval: Ideas, Influences, and Trends ofthe New Age. ACM Computing Surveys.2008,40(2):l一60.
[25] Yoo Hun-Woo, Jung She-Hwan. Extraction of Major Object Feature Using VQ Clusteringfor Content-based Image Retrieval. Pattern Recognition.2002,35(2):1115-1126.
[26] Lin Hwei-Jen, Kao Yang-Ta. A Study of Shape-based Image Retrieval. IEEE Computer.2004,22(2):645-662.
[27] Yu Honpug-Heather. Visual Image Retrieval on Compressed Domain with Q-Distance ImageSignal Process. International Journal of Computer Vision.2000,31(1):134-149.
[28] M. J. Swain, D. H. Ballard. Color Indexing.International Journal of ComputerVision.1991,7(1):11-32.
[29] E. Montiel. Texture Classification via Conditional Histograms. Pattern RecognitionLetters.2005,26(11):1740-1751.
[30]林克正,张彩华,刘丕娥.基于分块主颜色匹配的图像检索.计算机工程.2010,36(13):186-188.
[31] Zhans Dengsheng, Lu Guojun. Review of Shape Representation and Description Techniques.Pattem Recognition.2004,37(1):1-19.
[32] X. Q. Xu, D. J. Lee, S. Antani, et al. A Spine X-Bay Image Retrieval System UsingPartial Shape Matching. IEEE Trans on Information Technology in Biomedicine.2008,12(1):100-108.
[33] C. H. Wei, Y. Li, W. Y. Chau, et al. Trademark Image Retrieval Using Synthetic Featuresfor Describing Global Shape and Interior Structure. Pattem Recognition.2009,42(3):386—394.
[34]王向阳,陈景伟,于永健.一种基于彩色边缘综合特征的图像检索算法.模式识别与人工智能.2010,23(2):216-220.
[35] K. R. Castleman.数字图像处理.北京:电子工业出版社,1998.
[36]周明全,耿国华,韦娜.基于内容图像检索技术.北京：清华大学出版社，2007:22.
[37] N. Dalal, B. Triggs. Histograms of oriented gradients for human detection. Proceedingsof the IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR'05), San Diego, CA, USA,2005, Vol1:886-893.
[38] A. Bosch, A. Zisserman, X. Munoz. Representing shape with a spatial pyramid kernel.Proceedings of the6th ACM international conference on Image and video retrieval,Amsterdam, Dutch.2007:401-40
[39] Y. Rui, T. S. Huang, S. F. Chang. Image Retrieval: Current Techniques, PromisingDirections and Open Issues. Journal of Visual Communication and Image Representation.1999,10(4):39-62.
[40] M. Koskela, J. Laaksonen, Oja E. Comparison of Techniques for Content-based ImageRetrieval. Scandinavian Conference on Image Analysis, Bergen.2001:579-586.
[41] B. S. Manjunath, W. Y. Ma. Texture Features for Browsing and Retrieval of Image Data.IEEE Transactions on Pattern Analysis and Machine Intelligence.1996,18(8):837-842.
[42] B. Julesz. Textons: the elements of texture perception and their interactions. Nature290(5802).1981:91–97.
[43] B. Julesz. Texton gradients: the texton theory revisited. Biological Cybernetics.1986:245–251.
[44]魏宝刚，李向阳，鲁东明等.彩色分割研究进展.计算机科学.1999,26(4):59-62.
[45] F. G. Meyer, R. R. Coifman. Brushlets: A tool for directional image analysis and imagecompression. J. Of ACHA.1997,(5):147-187.
[46]章毓晋.图像工程上册-图像处理与分析.北京:清华大学出版社，1999.
[47] J. Beck. Effect of orientation and of shape similarity on perceptual grouping.Perceptual paychophysics.1(7):300-302.
[48] J. R. Bergen, B. Julesz. Parallel versus serial processing in pattern discrimination.Natural.1983,303(7):696-698.
[49] J. R. Bergen, E. H. Adelson. Early vision and texture perception using featuredistribution. Pattern Recognition.1999,32(3):447-486.
[50] G. H. Liu, L. Zhang, Y. K. Hou, Z. Y. Li, J. Y. Yang. Image retrieval based onmulti-texton histogram. Pattern Recognition.2010,43:2380-2389.