基于MPEG-7的图像检索技术
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摘要
随着多媒体时代的到来，越来越多的图像被广泛的使用，基于内容的图像检索(CBIR)技术成为近年来的一个研究热点。另一方面，由于信息的复杂多样，对其内容的描述缺乏统一标准，这就导致现有CBIR系统的通用性得不到保证，MPEG-7正是为规范多媒体内容描述而启动的国际标准，它将描述与信息内容相联系，使快速、有效的查询和访问各种多媒体成为可能。
本文的主要研究目标是基于MPEG-7的图像检索，取得了一些有意义的研究成果。主要内容包括：
·详细研究了MPEG-7中颜色描述符的提取方法，对其中的颜色布局和颜色结构描述符进行了检索实验和性能分析。
·提出了一种主颜色提取方法，与同样基于向量量化的主颜色提取方法相比取得了更好的检索效果，同时对其采用的分块加权方案改进效果明显。
·提出了基于结构量化直方图的图像检索方法，指出颜色结构直方图和全局直方图是结构量化直方图取极值量化步长的特例，通过检索实验对三者的性能进行了分析，实验结果显示量化步长取0.5时结构量化直方图的检索效果最好。
J’一西大学硕一}:学位沦文:基于MPEG一7的图像检索技术
·利用主颜色和颜色布局开发了一个实际的图像检索系统，同时
满足了全局和局部两种不同的相似需要，检索结果表明综合两种特征
的检索与使用单一特征相比效果更好。
Along with the arrival of the multimedia age, more and more images were used widely. Content-based image retrieval technology has been a research hot spot in the recent years. On the other hand, due to the complexity of the information, the lack of standard for the description of image content induces low interoperability among CBIR systems. To meet this challenge, MPEG-7 was started as a solution to the normative description of audiovisual content. MPEG-7 relates the description with its content, and enables the effective and efficient query and access to multimedia content.
In this paper, the main research objective is the study of image retrieval based on MPEG-7. Some significative conclusions are drawed from this study. The major contents include:
The extraction of color descriptors is reported in detailed. The color layout and color structure descriptor are used in retrieval test and the performance is discussed.
An extraction of dominant color is presented and it obtains better retrieval effectiveness compared with the extraction, which also base
on vector quantization. A method which adds the weight to blocks was applied to dominant color and the improved effect is obvious.
A method for image retrieval based on structuring quantized histogram is presented. The color structure histogram and color histogram are the specific cases of structuring quantized histogram, which is endowed an extreme quantized step. The performances of the three type of histogram are discussed through the retrieval test and results show that the performance of structuring quantized histogram is the best.
An actual CBIR system was developed based on dominant color and color layout, meeting global and local similarity needs. Retrieval results obtained from combined features are superior to that obtained from single feature.
本文的主要研究目标是基于MPEG-7的图像检索，取得了一些有意义的研究成果。主要内容包括：
·详细研究了MPEG-7中颜色描述符的提取方法，对其中的颜色布局和颜色结构描述符进行了检索实验和性能分析。
·提出了一种主颜色提取方法，与同样基于向量量化的主颜色提取方法相比取得了更好的检索效果，同时对其采用的分块加权方案改进效果明显。
·提出了基于结构量化直方图的图像检索方法，指出颜色结构直方图和全局直方图是结构量化直方图取极值量化步长的特例，通过检索实验对三者的性能进行了分析，实验结果显示量化步长取0.5时结构量化直方图的检索效果最好。
J’一西大学硕一}:学位沦文:基于MPEG一7的图像检索技术
·利用主颜色和颜色布局开发了一个实际的图像检索系统，同时
满足了全局和局部两种不同的相似需要，检索结果表明综合两种特征
的检索与使用单一特征相比效果更好。
Along with the arrival of the multimedia age, more and more images were used widely. Content-based image retrieval technology has been a research hot spot in the recent years. On the other hand, due to the complexity of the information, the lack of standard for the description of image content induces low interoperability among CBIR systems. To meet this challenge, MPEG-7 was started as a solution to the normative description of audiovisual content. MPEG-7 relates the description with its content, and enables the effective and efficient query and access to multimedia content.
In this paper, the main research objective is the study of image retrieval based on MPEG-7. Some significative conclusions are drawed from this study. The major contents include:
The extraction of color descriptors is reported in detailed. The color layout and color structure descriptor are used in retrieval test and the performance is discussed.
An extraction of dominant color is presented and it obtains better retrieval effectiveness compared with the extraction, which also base
on vector quantization. A method which adds the weight to blocks was applied to dominant color and the improved effect is obvious.
A method for image retrieval based on structuring quantized histogram is presented. The color structure histogram and color histogram are the specific cases of structuring quantized histogram, which is endowed an extreme quantized step. The performances of the three type of histogram are discussed through the retrieval test and results show that the performance of structuring quantized histogram is the best.
An actual CBIR system was developed based on dominant color and color layout, meeting global and local similarity needs. Retrieval results obtained from combined features are superior to that obtained from single feature.
引文
[1] T. Kato, Database architecture for content-based image retrieval, In Proceedings of SPIE: Image Storage and Retrieval Systems, Vol. 1662, 112-123, 1992
[2] Editor: J. Martinez, MPEG-7 Overview(Version 8), ISO/IEC JTC1/SC29/WG11, http://mpeg.telecomitalialab.com/standards/mpeg-7/mpeg-7.htm, 2002
[3] Rui, Y., Huang, T., Chang, SF, Image Retrieval: Current Techniques, Promising Directions, and Open Issues, Journal of Visual Communication and Image Representation, Vol.10(No.1), 39-62, 1999
[4] WY Ma, HJ Zhang, Content-based Image Indexing and Retrieval, In Handbook of Multimedia Computing, Borko Furht. Ed., CRC Press, 1998
[5] Swain, M., Ballard, D., Color Indexing, International Journal of Computer Vision, Vol.7(No.1), 11-32, 1991
[6] 周文昭，夏定元，周曼丽，基于内容的图像检索系统的最新进展，计算机工程与应用，Vol．39(No．26)，112-124，2003
[7] M. Stricker, M. Orengo, Similarity of color images, In Proceedings of SPIE-storage and retrieval for image and video databases Ⅲ, Vol.2420, 381-392, 1995
[8] Constantin Vertan, Nozha Boujemaa, Using Fuzzy Histograms and Distances for Color Image Retrieval, CIR 2000, Brighton http://www-roeq.inria.fr/imedia/Articles/cir2000.pdf
[9] Ka-Man Wong, Chun-Ho Cheung, Lai-Man Po, Merged-color histogram for color image retrieval, Proceeding of 2002 IEEE International Conference on Image Processing, Vol. 3, 949-952, Sept. 2002, New York, USA
[10] R. Smith, S.-F. Chang, Tools and techniques for color image retrieval, In Symposium on Electronic Imaging: Science and Technology-Storage & Retrieval for Image and Video Databases Ⅳ, Vol.2670, San Jose, CA, February 1996 http://www.ctr.columbia.edu/-j rsmith/html/pubs/tatfcir/color.html
[11] R. M. Haralick, Statistical and structural approaches to texture, Proceedings of IEEE, Vol. 67(No. 5), 786-804, 1979
[12] John P. Eakins, Margaret E. Graham, Content-Based Image Retrieval, A Report to the JISC Technology Application Programme, Technical report, Institute for Image Data Research, University of Northumbria at Newcastle, 1999
[13] Editor: Neil Day, MPEG-7 Applications, ISO/IEC JTC 1/SC29/WG11, 2002
[14] IBM, VideoAnnEx Annotation Tool http://www.research.ibm.com/VideoAnnEx/
[15] 章毓晋，基于内容的视觉信息检索，科学出版社，2003
[16] M. Flickner, H. Sawhney, W. Niblack, et al., Query by image and video content: The QBIC system, IEEE Computer, Vol.28(No. 9), 23-32, 1995
[17] 陈纯，计算机图像处理技术与算法，清华大学出版社，2003
[18] Vellaikal, A., Jay Kuo, CC and Dao, S., Content-Based Retrieval of Color and Multispectral Images Using Joint Spatial-Spectral Indexing, SPIE Vol. 2606, 232-243.
[19] J. R. Smith, S.-F.Chang, Single color extraction and image query, In IEEE Pine. Int. Conf. Image Processing, Washington, DC, October 1995 http://www.ctr.columbia.edu/～jrsmith/html/pubs/ICIP-95-2/single_1.html
[20] M. S. Kankanhalli, B. M. Mehtre, Jiankang Wu, Cluster-based color matching for image retrieval, Pattern Recognition, Vol. 29(No. 4), 701-708, 1996
[21] Pass G., R. Zabih, J Miller, Comparing Images Using Color Coherence Vectors, ACM Conference on Multimedia, Boston, Massachusetts, November 1996 http://www.cs.cornell.edu/rdz/Papers/Archive/mm96-final.pdf
[22] R. M. Haralick, K. Shanmugam, I. Dinstein. Texture features for image classification, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 3(No. 6), 610-621, 1973
[23] H. Tamura, S. Mori, T. Yamawaki. Texture features corresponding to visual perception, IEEE Transactions on Systems, Man, and Cybernetics, Vol SMC-8, No 6, 460-473, 1978
[24] Bimbo A., Visual Information Retrieval, Morgan Kaufmann, Inc. 1999
[25] Berman AP, Shapiro LG, Triangle-inequality-based pruning algorithms with triangle tries, Proc. SPIE Storage and Retrieval for Image and Video Databases,
Vol. 3656, 356-365, Jan. 1999
[26] B.S.Manjunath, Jens-Rainer Ohm, Vinod V.Vasudevan, et al., Color and texture descriptors. IEEE Transactions on circuits and systems for video technology, Vol.11 (No. 6), June 2001
[27] Gonzalez RC, Woods RE, Digital Image Processing, Addison-Wesley, 1992
[28] Mehtre B.M., M.S. Kankanhalli, A. Desai Narasimhalu, et al., Color matching for image retrieval, Pattern Recognition Letters, Vol. 16(No. 3), 325-331, 1995
[29] J.R.Bach, C.Fuller, A.Gupta et al., The Virage image search engine: An open framework for image management, Proc. Storage and Retrieval for Image and Video Data- bases, SPIE Vol. 2670, 76-87, 1996
[30] J.R. Smith, S.-F. Chang, Visual SEEK: A fully automated content-based image query system, ACM Multimedia Conference, Boston, 87-98, Nov. 1996 http://cglab.cs.nthu.edu.tw/MMIS2003/download/paper/VisualSEEK.pdf
[31] Thomas S.Huang, Sharad Mehrotra, Kannan Ramchandran, Multimedia analysis and retrieval system(MARS) project, In Proc of 33rd Annual Clinic on Library Application of Data Processing-Digital Image Access and Retrieval, 1996
[32] Yong Rui, Thomas S. Huang, Michael Ortega, et al., Relevance feedback: A power tool in interactive content-based image retrieval, IEEE Trans. on Circuits and Systems for Video Technology, Vol. 8(No. 5) 644-655, Sep. 1998
[33] Editor: F. Pereira, MPEG-7 Requirements Document V.17, ISO/IEC JTC 1/SC29/WG11, Doe. N4981, 2002
[34] Text of ISO/IEC 15938-1 Multimedia Content Description Interface—Part 1: Systems, ISO/IEC/JTC1/SC29/WG11, Oct. 2000
[35] Text of ISO/IEC 15938-2 Multimedia Content Description Interface—Part 2: Description Definition Language, ISO/IEC/JTC1/SC29/WG11, Oct. 2000
[36] Text of ISO/IEC 15938-3 Multimedia Content Description Interface—Part 3: Visual, ISO/IEC/JTC 1/SC29/WG11, Oct. 2000
[37] Text of ISO/IEC 15938-4 Multimedia Content Description Interface—Part 4: Audio, ISO/IEC/JTCI/SC29/WG11, Oct. 2000
[38] Text of ISO/IEC 15938-5 Multimedia Content Description Interface——Part5: Multimedia Description Schemes, ISO/IEC/JTC1/SC29/WG11, Oct.2000
[39] 李岚，冯刚，MPEG-7与基于内容的图像检索，计算机工程与应用，No．17，98—102，2002
[40] Jens-Rainer Ohm, F. Bunjamin, W. Liebsch, A Visual Search Engine for Distributed Image and Video Database Retrieval Applications, in Visual Information and Information Systems-VISUAL'99, Amsterdam, Springer Lecture Notes in Computer Science 1614, 187-194, June 1999 http://www.ient.rwth-aachen.de/team/ohm/publi/visua199.pdf
[41] Yining Deng, B.S.Manjunath, Charles Kenney, et al., An Efficient Color Representation for Image Retrieval, IEEE Transactions on image processing, Vol.10(No.1), Jan. 2001
[42] Yining Deng, Charles Kenney, Michael S. Moore, et al., Peer Group Filtering and Perceptual Color Image Quantization, In: IEEE International Symposium on Circuits and Systems, Vol.4, 21-24, 1999
[43] Michel Lantagne, Marc Parizeau, Robert Bergevin, VIP: Vision tool for comparing Images of People, Vision Interface 2003 http://vision.gel.ulaval.ca/～lantagne/LantagneVI2003.pdf
[44] 曹莉华，柳伟，李国辉，基于多种主色调的图像检索算法研究与实现，计算机研究与发展，Vol．36(No．1)，1999
[45] J.R. Smith, Integrated spatial and feature image systems: Retrieval, analysis and compression, PhD thesis, Columbia University, 1997 http://disney.ctr.columbia.edu/jrsthesis/
[46] 袁昕，朱淼良，基于主色匹配的图像检索系统，计算机辅助设计与图形学学报，Vol．12(No．12)，2000
[47] Hafner, J., Sawhney, HS, Equitz, W., et al., Efficient Color Histogram Indexing for Quadratic Form Distance Eunctions, In IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 17(No. 7), 729-739, July 1995
[2] Editor: J. Martinez, MPEG-7 Overview(Version 8), ISO/IEC JTC1/SC29/WG11, http://mpeg.telecomitalialab.com/standards/mpeg-7/mpeg-7.htm, 2002
[3] Rui, Y., Huang, T., Chang, SF, Image Retrieval: Current Techniques, Promising Directions, and Open Issues, Journal of Visual Communication and Image Representation, Vol.10(No.1), 39-62, 1999
[4] WY Ma, HJ Zhang, Content-based Image Indexing and Retrieval, In Handbook of Multimedia Computing, Borko Furht. Ed., CRC Press, 1998
[5] Swain, M., Ballard, D., Color Indexing, International Journal of Computer Vision, Vol.7(No.1), 11-32, 1991
[6] 周文昭，夏定元，周曼丽，基于内容的图像检索系统的最新进展，计算机工程与应用，Vol．39(No．26)，112-124，2003
[7] M. Stricker, M. Orengo, Similarity of color images, In Proceedings of SPIE-storage and retrieval for image and video databases Ⅲ, Vol.2420, 381-392, 1995
[8] Constantin Vertan, Nozha Boujemaa, Using Fuzzy Histograms and Distances for Color Image Retrieval, CIR 2000, Brighton http://www-roeq.inria.fr/imedia/Articles/cir2000.pdf
[9] Ka-Man Wong, Chun-Ho Cheung, Lai-Man Po, Merged-color histogram for color image retrieval, Proceeding of 2002 IEEE International Conference on Image Processing, Vol. 3, 949-952, Sept. 2002, New York, USA
[10] R. Smith, S.-F. Chang, Tools and techniques for color image retrieval, In Symposium on Electronic Imaging: Science and Technology-Storage & Retrieval for Image and Video Databases Ⅳ, Vol.2670, San Jose, CA, February 1996 http://www.ctr.columbia.edu/-j rsmith/html/pubs/tatfcir/color.html
[11] R. M. Haralick, Statistical and structural approaches to texture, Proceedings of IEEE, Vol. 67(No. 5), 786-804, 1979
[12] John P. Eakins, Margaret E. Graham, Content-Based Image Retrieval, A Report to the JISC Technology Application Programme, Technical report, Institute for Image Data Research, University of Northumbria at Newcastle, 1999
[13] Editor: Neil Day, MPEG-7 Applications, ISO/IEC JTC 1/SC29/WG11, 2002
[14] IBM, VideoAnnEx Annotation Tool http://www.research.ibm.com/VideoAnnEx/
[15] 章毓晋，基于内容的视觉信息检索，科学出版社，2003
[16] M. Flickner, H. Sawhney, W. Niblack, et al., Query by image and video content: The QBIC system, IEEE Computer, Vol.28(No. 9), 23-32, 1995
[17] 陈纯，计算机图像处理技术与算法，清华大学出版社，2003
[18] Vellaikal, A., Jay Kuo, CC and Dao, S., Content-Based Retrieval of Color and Multispectral Images Using Joint Spatial-Spectral Indexing, SPIE Vol. 2606, 232-243.
[19] J. R. Smith, S.-F.Chang, Single color extraction and image query, In IEEE Pine. Int. Conf. Image Processing, Washington, DC, October 1995 http://www.ctr.columbia.edu/～jrsmith/html/pubs/ICIP-95-2/single_1.html
[20] M. S. Kankanhalli, B. M. Mehtre, Jiankang Wu, Cluster-based color matching for image retrieval, Pattern Recognition, Vol. 29(No. 4), 701-708, 1996
[21] Pass G., R. Zabih, J Miller, Comparing Images Using Color Coherence Vectors, ACM Conference on Multimedia, Boston, Massachusetts, November 1996 http://www.cs.cornell.edu/rdz/Papers/Archive/mm96-final.pdf
[22] R. M. Haralick, K. Shanmugam, I. Dinstein. Texture features for image classification, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 3(No. 6), 610-621, 1973
[23] H. Tamura, S. Mori, T. Yamawaki. Texture features corresponding to visual perception, IEEE Transactions on Systems, Man, and Cybernetics, Vol SMC-8, No 6, 460-473, 1978
[24] Bimbo A., Visual Information Retrieval, Morgan Kaufmann, Inc. 1999
[25] Berman AP, Shapiro LG, Triangle-inequality-based pruning algorithms with triangle tries, Proc. SPIE Storage and Retrieval for Image and Video Databases,
Vol. 3656, 356-365, Jan. 1999
[26] B.S.Manjunath, Jens-Rainer Ohm, Vinod V.Vasudevan, et al., Color and texture descriptors. IEEE Transactions on circuits and systems for video technology, Vol.11 (No. 6), June 2001
[27] Gonzalez RC, Woods RE, Digital Image Processing, Addison-Wesley, 1992
[28] Mehtre B.M., M.S. Kankanhalli, A. Desai Narasimhalu, et al., Color matching for image retrieval, Pattern Recognition Letters, Vol. 16(No. 3), 325-331, 1995
[29] J.R.Bach, C.Fuller, A.Gupta et al., The Virage image search engine: An open framework for image management, Proc. Storage and Retrieval for Image and Video Data- bases, SPIE Vol. 2670, 76-87, 1996
[30] J.R. Smith, S.-F. Chang, Visual SEEK: A fully automated content-based image query system, ACM Multimedia Conference, Boston, 87-98, Nov. 1996 http://cglab.cs.nthu.edu.tw/MMIS2003/download/paper/VisualSEEK.pdf
[31] Thomas S.Huang, Sharad Mehrotra, Kannan Ramchandran, Multimedia analysis and retrieval system(MARS) project, In Proc of 33rd Annual Clinic on Library Application of Data Processing-Digital Image Access and Retrieval, 1996
[32] Yong Rui, Thomas S. Huang, Michael Ortega, et al., Relevance feedback: A power tool in interactive content-based image retrieval, IEEE Trans. on Circuits and Systems for Video Technology, Vol. 8(No. 5) 644-655, Sep. 1998
[33] Editor: F. Pereira, MPEG-7 Requirements Document V.17, ISO/IEC JTC 1/SC29/WG11, Doe. N4981, 2002
[34] Text of ISO/IEC 15938-1 Multimedia Content Description Interface—Part 1: Systems, ISO/IEC/JTC1/SC29/WG11, Oct. 2000
[35] Text of ISO/IEC 15938-2 Multimedia Content Description Interface—Part 2: Description Definition Language, ISO/IEC/JTC1/SC29/WG11, Oct. 2000
[36] Text of ISO/IEC 15938-3 Multimedia Content Description Interface—Part 3: Visual, ISO/IEC/JTC 1/SC29/WG11, Oct. 2000
[37] Text of ISO/IEC 15938-4 Multimedia Content Description Interface—Part 4: Audio, ISO/IEC/JTCI/SC29/WG11, Oct. 2000
[38] Text of ISO/IEC 15938-5 Multimedia Content Description Interface——Part5: Multimedia Description Schemes, ISO/IEC/JTC1/SC29/WG11, Oct.2000
[39] 李岚，冯刚，MPEG-7与基于内容的图像检索，计算机工程与应用，No．17，98—102，2002
[40] Jens-Rainer Ohm, F. Bunjamin, W. Liebsch, A Visual Search Engine for Distributed Image and Video Database Retrieval Applications, in Visual Information and Information Systems-VISUAL'99, Amsterdam, Springer Lecture Notes in Computer Science 1614, 187-194, June 1999 http://www.ient.rwth-aachen.de/team/ohm/publi/visua199.pdf
[41] Yining Deng, B.S.Manjunath, Charles Kenney, et al., An Efficient Color Representation for Image Retrieval, IEEE Transactions on image processing, Vol.10(No.1), Jan. 2001
[42] Yining Deng, Charles Kenney, Michael S. Moore, et al., Peer Group Filtering and Perceptual Color Image Quantization, In: IEEE International Symposium on Circuits and Systems, Vol.4, 21-24, 1999
[43] Michel Lantagne, Marc Parizeau, Robert Bergevin, VIP: Vision tool for comparing Images of People, Vision Interface 2003 http://vision.gel.ulaval.ca/～lantagne/LantagneVI2003.pdf
[44] 曹莉华，柳伟，李国辉，基于多种主色调的图像检索算法研究与实现，计算机研究与发展，Vol．36(No．1)，1999
[45] J.R. Smith, Integrated spatial and feature image systems: Retrieval, analysis and compression, PhD thesis, Columbia University, 1997 http://disney.ctr.columbia.edu/jrsthesis/
[46] 袁昕，朱淼良，基于主色匹配的图像检索系统，计算机辅助设计与图形学学报，Vol．12(No．12)，2000
[47] Hafner, J., Sawhney, HS, Equitz, W., et al., Efficient Color Histogram Indexing for Quadratic Form Distance Eunctions, In IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 17(No. 7), 729-739, July 1995
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