基于内容的遥感影像库检索关键技术研究
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摘要
遥感影像数据作为数字地球各项重大计划建设中的基础数据，其快速浏览和高效检索是遥感影像信息提取和共享的重要手段；基于内容的图像检索技术作为从试图理解图像内容的角度有效管理和利用图像数据库中信息的手段，已经成为图像数据库、计算机视觉等领域的研究热点和未来信息高速公路、数字图书馆等重大项目中的关键技术，为解决大型遥感影像数据库的信息提取难题提供了新的契机。然而，遥感影像数据的多样性、复杂性和海量性无疑对基于内容的遥感影像库检索、特别是Web环境下的应用提出了巨大的挑战。基于内容的遥感影像库检索技术是遥感影像处理、图像数据库技术、计算机视觉、模式识别等领域相结合的国际前沿课题，对于促进遥感影像信息的提取和共享，具有十分重要的理论意义和实用价值。本文旨在针对基于内容的遥感影像库检索的关键技术，提出一些创新性思路和方法，并分别从理论和技术的角度对其价值和实用性予以分析和验证。本文的主要内容包括：
①系统地归纳和分析了目前基于内容的遥感影像检索的国内外相关研究计划及主要研究成果，总结了基于内容的遥感影像库检索涉及到的各项关键技术，分析了基于内容的图像检索技术在遥感领域的应用与在多媒体和医学等其它领域的应用相比所面临的困难，并指出了解决问题的出发点。
②提出遥感影像纹理特征的多尺度描述以及合理的遥感影像数据分块组织策略，是支持基于纹理特征的遥感影像库检索的关键。从分析传统的遥感影像数据分块组织策略的弊端以及小波变换在遥感影像纹理特征提取中的意义入手，提出了基于Nona-tree结构和小波直方图创建同质纹理特征库的方法和检索实现方法，既利用了Nona-tree在分块组织遥感影像数据时能够在检索精度、检索效率和存储空间之间达到较好平衡的优势，同时利用了小波直方图提取遥感影像多尺度纹理特征的高效、快捷的特性，并通过实验进行了充分的验证。此外，本文将多进制小波理论引入遥感影像纹理特征描述，从理论上分析了多进制小波与常规的二进制小波相比在描述图像特征方面的优势，提出了采用多进制小波直方图和多进制快速小波直方图进行遥感影像纹理特征提取以及相似性度量的方法，通过实验对比了基于多进制小波直方图和二进制小波直方图的遥感影像纹理特征检索精度和检索效率。
③结合目前基于形状的图像检索技术的研究现状以及遥感影像上目标识别的困难，探索性提出在相关领域技术发展的现有水平下实现基于目标形状的遥感影像检索的可行方案，避开了由于目前遥感影像目标全自动识别的困难而对遥感影像形状检索造成的影响，为利用基于形状的图像检索技术的现有理论和方法创造了条件，克服了模板匹配方法要求精确匹配及计算量大的局限性。从理论和技
术的角度研究了在基于目标形状的遥感影像检索中，应用基于小波变换模极大值
和多尺度形态学的边缘检测方法以及基于不变相对矩的轮廓特征描述方法的具
体实现技术流程，通过实验充分验证了本文方法的可行性，并分析了检索性能。
④从分析基于内容的图像检索技术在计算机科学中的抽象本质以及目前基
于内容的图像检索技术中常用索引机制的局限性入手，强调了研究基于距离的度
量空间高维索引结构的重要意义。对现有的各种度量空间高维索引结构做了分
类，并从分析构造和搜索算法的角度研究了将典型的基于距离的度量空间高维索
引结构VP一tree及其改进算法MVP一tree应用于遥感影像可视化特征相似性索引
的具体实现方法，并通过实验对其性能进行了充分的验证和分析，为克服传统的
降维技术以及多维索引结构用于可视化特征索引的不足提供了可靠的实践依据。
最后进一步分析了改进广义MvP一tree性能的两个出发点，提出通过参数优化模
型有利于改进广义MVP一tree的搜索性能，并提出基于遗传算法的性能改进思路。
⑤设计了基于内容的遥感影像库检索原型系统的三层B/S模式体系结构和
支持基于内容遥感影像库检索的层状数据模型，探讨了主要功能模块的实现方法
并给出部分函数原型。
总结本文的研究工作，主要贡献及创新点可概括如下:
①提出了基于Nona一tree结构和小波直方图的遥感影像库纹理特征检索新
方法，保证了影像检索在效率、精度和存储空间之间的平衡。
②提出了采用多进制小波直方图技术提取遥感影像纹理特征的方法，为充
分利用多进制小波变换在提取图像细节信息方面的优势提供了技术路线。
③探索性开展了基于目标形状特征的遥感影像检索研究，从理论和技术的
角度研究了基于小波变换和数学形态学的边缘检测算法和基于不变相对矩的轮
廓特征提取方法，为有效开展基于目标形状特征的遥感影像检索提供了可行的技
术方案。
④将典型的基于距离的度量空间高维索引结构vP一tree及其改进算法
MVP一t ree应用于遥感影像可视化特征的相似性索引;进一步提出通过参数优化模型
提高广义MVP一tree搜索性能的思路。
本文在结论部分指出需要进一步研究的问题。
Remote Sensing Data are basic data in digital earth development and its quick browsing and efficient retrieval are important means of remote sensing information extraction and sharing. As an effective means of manage and utilize image database information according to comprehension of images themselves, content-based image retrieval (CBIR) has become one of the most active researches in image databases, computer vision etc. and a key technology of information high way and digital library. CBIR provides new chance to solve the problem of information extraction from large remote sensing image database. However, the diversity and complexity of remote sensing image and the enormous data volume as well are big challenge of valid retrieval from remote sensing image databases, especially under web environment. Content-based retrieval of remote sensing database is a hot topic by integrating multiple disciplines including remote sensing image processing, image databases, computer vision and pattern recognition etc. a
nd has gotten international considerable attention. Research on it has important meaning in theory and practice for promoting remote sensing information acquisition and sharing. This paper intends to put forward some new thoughts and methods on key technologies for content-based retrieval of remote sensing image database and to validate its efficiency and practicability through theory and practice. Main research and concrete work include five aspects:
1. Firstly, domestic and foreign research projects and state-of-the-arts of content-based retrieval from remote sensing image databases are induced systematically. Then concerned key technologies are summarized and main obstacles compared with other applications of content-based image retrieval are analyzed. Based on them, key aspects to solution are pointed out.
2. Secondly, this paper points out multi-scale texture feature extraction and reasonable strategies of block-based data organization are two important areas to support
texture-based retrieval from remote sensing image databases. The limitation of traditional remote sensing data organization and the significance of wavelet transform in texture feature extraction of remote sensing images are analyzed and based on them, a method of creating homogeneous texture feature databases and retrieval implementation by integrating Nona-tree data structure and wavelet histogram technology is presented. It can take advantage of the powerful ability of Nona-tree, which can reach balance among precision, efficiency and storage, and that of wavelet histogram, which can extract texture feature with high efficiency. Experimental results are given to prove its efficiency. Besides, this paper introduces M-band wavelet theory to texture feature representation and analyzes its advantage compared with traditional two-band wavelet transform in theory. Feature extraction and similarity calculation by adopting M-band wavelet histogram technologies are presented in detail. Retrieval performance based on 2-band wavelet histogram and M-band wavelet histogram are tested and compared.
3. Thirdly, by considering the difficulty of shape-based retrieval at present and automatic man-made objects extraction synthetically, this paper explores the feasible strategy of shape-based retrieval from remote sensing images at current level. This strategy avoids the impact due to the big difficult of automatic manmade object discrimination in current state and overcomes the limits of exact matching and huge computational volume aroused by template matching. This paper studies the concrete implementation flow of contour-based retrieval based on wavelet transform modulus maxima (WTMM), multi-scale morphology and invariant relative moments. Also, experimental results are given to validate the feasibility of our strategy and the corresponding retrieval performance is analyzed.
4. Fourthly, by analyzing the abstract essence of CBIR in computer science and current solution of visual feature index are analyzed. Further
①系统地归纳和分析了目前基于内容的遥感影像检索的国内外相关研究计划及主要研究成果，总结了基于内容的遥感影像库检索涉及到的各项关键技术，分析了基于内容的图像检索技术在遥感领域的应用与在多媒体和医学等其它领域的应用相比所面临的困难，并指出了解决问题的出发点。
②提出遥感影像纹理特征的多尺度描述以及合理的遥感影像数据分块组织策略，是支持基于纹理特征的遥感影像库检索的关键。从分析传统的遥感影像数据分块组织策略的弊端以及小波变换在遥感影像纹理特征提取中的意义入手，提出了基于Nona-tree结构和小波直方图创建同质纹理特征库的方法和检索实现方法，既利用了Nona-tree在分块组织遥感影像数据时能够在检索精度、检索效率和存储空间之间达到较好平衡的优势，同时利用了小波直方图提取遥感影像多尺度纹理特征的高效、快捷的特性，并通过实验进行了充分的验证。此外，本文将多进制小波理论引入遥感影像纹理特征描述，从理论上分析了多进制小波与常规的二进制小波相比在描述图像特征方面的优势，提出了采用多进制小波直方图和多进制快速小波直方图进行遥感影像纹理特征提取以及相似性度量的方法，通过实验对比了基于多进制小波直方图和二进制小波直方图的遥感影像纹理特征检索精度和检索效率。
③结合目前基于形状的图像检索技术的研究现状以及遥感影像上目标识别的困难，探索性提出在相关领域技术发展的现有水平下实现基于目标形状的遥感影像检索的可行方案，避开了由于目前遥感影像目标全自动识别的困难而对遥感影像形状检索造成的影响，为利用基于形状的图像检索技术的现有理论和方法创造了条件，克服了模板匹配方法要求精确匹配及计算量大的局限性。从理论和技
术的角度研究了在基于目标形状的遥感影像检索中，应用基于小波变换模极大值
和多尺度形态学的边缘检测方法以及基于不变相对矩的轮廓特征描述方法的具
体实现技术流程，通过实验充分验证了本文方法的可行性，并分析了检索性能。
④从分析基于内容的图像检索技术在计算机科学中的抽象本质以及目前基
于内容的图像检索技术中常用索引机制的局限性入手，强调了研究基于距离的度
量空间高维索引结构的重要意义。对现有的各种度量空间高维索引结构做了分
类，并从分析构造和搜索算法的角度研究了将典型的基于距离的度量空间高维索
引结构VP一tree及其改进算法MVP一tree应用于遥感影像可视化特征相似性索引
的具体实现方法，并通过实验对其性能进行了充分的验证和分析，为克服传统的
降维技术以及多维索引结构用于可视化特征索引的不足提供了可靠的实践依据。
最后进一步分析了改进广义MvP一tree性能的两个出发点，提出通过参数优化模
型有利于改进广义MVP一tree的搜索性能，并提出基于遗传算法的性能改进思路。
⑤设计了基于内容的遥感影像库检索原型系统的三层B/S模式体系结构和
支持基于内容遥感影像库检索的层状数据模型，探讨了主要功能模块的实现方法
并给出部分函数原型。
总结本文的研究工作，主要贡献及创新点可概括如下:
①提出了基于Nona一tree结构和小波直方图的遥感影像库纹理特征检索新
方法，保证了影像检索在效率、精度和存储空间之间的平衡。
②提出了采用多进制小波直方图技术提取遥感影像纹理特征的方法，为充
分利用多进制小波变换在提取图像细节信息方面的优势提供了技术路线。
③探索性开展了基于目标形状特征的遥感影像检索研究，从理论和技术的
角度研究了基于小波变换和数学形态学的边缘检测算法和基于不变相对矩的轮
廓特征提取方法，为有效开展基于目标形状特征的遥感影像检索提供了可行的技
术方案。
④将典型的基于距离的度量空间高维索引结构vP一tree及其改进算法
MVP一t ree应用于遥感影像可视化特征的相似性索引;进一步提出通过参数优化模型
提高广义MVP一tree搜索性能的思路。
本文在结论部分指出需要进一步研究的问题。
Remote Sensing Data are basic data in digital earth development and its quick browsing and efficient retrieval are important means of remote sensing information extraction and sharing. As an effective means of manage and utilize image database information according to comprehension of images themselves, content-based image retrieval (CBIR) has become one of the most active researches in image databases, computer vision etc. and a key technology of information high way and digital library. CBIR provides new chance to solve the problem of information extraction from large remote sensing image database. However, the diversity and complexity of remote sensing image and the enormous data volume as well are big challenge of valid retrieval from remote sensing image databases, especially under web environment. Content-based retrieval of remote sensing database is a hot topic by integrating multiple disciplines including remote sensing image processing, image databases, computer vision and pattern recognition etc. a
nd has gotten international considerable attention. Research on it has important meaning in theory and practice for promoting remote sensing information acquisition and sharing. This paper intends to put forward some new thoughts and methods on key technologies for content-based retrieval of remote sensing image database and to validate its efficiency and practicability through theory and practice. Main research and concrete work include five aspects:
1. Firstly, domestic and foreign research projects and state-of-the-arts of content-based retrieval from remote sensing image databases are induced systematically. Then concerned key technologies are summarized and main obstacles compared with other applications of content-based image retrieval are analyzed. Based on them, key aspects to solution are pointed out.
2. Secondly, this paper points out multi-scale texture feature extraction and reasonable strategies of block-based data organization are two important areas to support
texture-based retrieval from remote sensing image databases. The limitation of traditional remote sensing data organization and the significance of wavelet transform in texture feature extraction of remote sensing images are analyzed and based on them, a method of creating homogeneous texture feature databases and retrieval implementation by integrating Nona-tree data structure and wavelet histogram technology is presented. It can take advantage of the powerful ability of Nona-tree, which can reach balance among precision, efficiency and storage, and that of wavelet histogram, which can extract texture feature with high efficiency. Experimental results are given to prove its efficiency. Besides, this paper introduces M-band wavelet theory to texture feature representation and analyzes its advantage compared with traditional two-band wavelet transform in theory. Feature extraction and similarity calculation by adopting M-band wavelet histogram technologies are presented in detail. Retrieval performance based on 2-band wavelet histogram and M-band wavelet histogram are tested and compared.
3. Thirdly, by considering the difficulty of shape-based retrieval at present and automatic man-made objects extraction synthetically, this paper explores the feasible strategy of shape-based retrieval from remote sensing images at current level. This strategy avoids the impact due to the big difficult of automatic manmade object discrimination in current state and overcomes the limits of exact matching and huge computational volume aroused by template matching. This paper studies the concrete implementation flow of contour-based retrieval based on wavelet transform modulus maxima (WTMM), multi-scale morphology and invariant relative moments. Also, experimental results are given to validate the feasibility of our strategy and the corresponding retrieval performance is analyzed.
4. Fourthly, by analyzing the abstract essence of CBIR in computer science and current solution of visual feature index are analyzed. Further
引文
[1] 徐冠华．构筑数字地球，促进中国和全球的可持续发展，"Towards Digital Earth",1999：6～10．
[2] 路甬祥．合作开发数字地球，共享全球数据资源，"Towards Digital Earth",1999：3～5．
[3] 赵霈生．Internet GIS领域软件体系结构研究[博士论文]．中科院遥感应用研究所，2000．
[4] 杨崇俊．数字地球研究与中国对策．2000高技术发展报告，2000：152～156
[5] 杨崇俊．在互联网络上能找到地理空间数据吗?陈述彭．主编．数字地球百问，北京：科学出版社，1999：176～177．
[6] Y.Rui, T.S.Huang and Shih-Fu Chang. Image retrieval: current techniques, promising directions, and open issues, Journal of Visual Communication and Image Representation, 1999.10:39～62.
[7] ISO/IEC JTC 1/SC 29/WG11/N4063, MPEG-7 Multimedia Description Schemes XM (Version 10), 2001.
[8] http://farside.gsfc.nasa.gov/ISTO/DLT/NSF_pr.html.
[9] W.Niblack, R.Barber et al. The QBIC project: Querying images by content using color, texture and shape, in Proc. SPIE Storage and Retrieval for Image and Video Databases, 1994.
[10] M.Flickner, H.Sawhney, W.Niblack et al.Query by image and video content: The QBIC system, IEEE Computer, 1995.
[11] W.Equitz and W.Niblack, Retrieving Images from a Database using Texture--Alogrithms from the QBIC System, Technical Report RJ9805, Computer Science, IBM Research Report, 1994.
[12] J.R.Bach, C.Fuller, A.Gupta, et al. The Virage image search engine: An open framework for image management, in Proc.SPIE Storage and Retrieval for Image and Video Databases. 1996.
[13] A.Gupta and R.Jain, Visual information retrieval, Communications of the ACM. 40(5), 1997.
[14] http://vrw/excalib.com/cgi-bin/sdk/cst/cst2.bat.
[15] J.R.Smith and S.-F.Chang, Querying by color regions using the VisualSEEk content-based visual querysystem (M.T.Maybury, Ed.), in Intelligent Multimedia Information Retrieval, 1996.
[16] J.R.Smith and S.-F.Chang, Visualseek: A fully automated content-based image query system, in Proc. ACM Multimedia96,1996.
[17] J.R.Smith and S.-F.Chang, Visually searching the web for content, IEEE Multimedia Magazine, 1997, 4(3): 12～20.
[18] T.S.Huang, S.Mehrotra, and K.Ramachandran, Multimedia analysis and retrieval system (MARS) project, in Proc.of 33rd Annual Clinicon Library Application of Data Processing Digital Image Access and Retrieval, 1996.
[19] S.Mehrotra, K.Chakrabarti, M.Ortega, Y.Rui and T.S.Huang, Multimedia analysis and retrieval system, in Proc.of the 3rd Int. Workshop on Information Retrieval Systems, 1997.
[20] http://elib.cs.berkeley.edu/.
[21] http://public.lanl.gov/kelly/CANDID/index.shtml.
[22] http://www.alexandria.ucsb.edu/.
[23] http://www.intsci.ac.cn/image/mires.html.
[24] M.Swain and D.Ballard, Color indexing, International Journal of Computer Vision 7(1), 1991.
[25] M.Stricke rand M.Orengo,Similarity of color images,in Proc. SPIE Storage and Retrieval forImage and Video Databases, 1995.
[26] J.R.Smith and S.-F.Chang,Single color extraction and image query, in Proc. IEEE Int. Conf. on Image Proe.,1995.
[27] J.R.Smith and S.-F.Chang,Tools and techniques for color image retrieval,in IS&T/SPIE Proceedings,Vol.2670,Storage & Retrieval for Image and Video Databases Ⅳ, 1995.
[28] R. M. Haraliek,K. Shanmugam,and I. Dinstein,Texture features for image classification,IEEE Trans. On Sys. Man. and Cyb. SMC-3(6),1973.
[29] H. Tamura,S. Mori,and T. Yamawaki,Texture features corresponding to visual perception,IEEE Trans. On Sys.,Man. and Cyb. SMC-8(6),1978.
[30] G C. Cross and A. K. Jain,Markov random field texture models,IEEE Trans. Part. Recog. and Mach. Intell. 1983,5:25～39.
[31] A. R Pentland,Fractal-based description of natural scenes,IEEE Trans. Patt. Recog. and Mach. Intell. 1984 6(6):661～674.
[32] J. R.Smith and S.-F. Chang,Automated binary texture feature sets for image retrieval,in Proc. ICASSP-96,Atlanta,GA, 1996.
[33] T. Chang and C.-C. J. Kuo,Texture analysis and classification with tree-structured wavelet transform,IEEE Trans. Image Proc. 1993,2(4):429～441.
[34] A. Laine and J. Fan,Texture Classification by wavelet packet signatures,IEEE Trans. Part. Recog. and Mach. Intell. 1993,15(11): 1186～1191.
[35] M. H. Gross,R. Koch,L. Lippert,and A. Dreger, Multiscale image texture analysis in wavelet spaces,in Proc. IEEE Int. Conf. on Image Proc., 1994.
[36] A. Kundu and J.-L. Chen, Texture classification using qmf bank-based subband decomposition,CVGIP: Graphical Models and Image Processing. 1992,54(5):369～384.
[37] J. R. Smith and S. F. Chang,Transform features for texture classification and
discrimination in large image databases,in Proc. IEEE Int. Conf. on Image Proc., 1994.
[38] K. S.Thyagarajan,T. Nguyen,and C. Persons,A maximum likelihood aproach to texture classification using wavelet transform,in Proc. IEEE Int. Conf. on Image Proc., 1994.
[39] M. K. Hu,Visual pattern recognition by moment invariants,computer methods in image analysis,IRETransactions on Information Theory 8,1962.
[40] L. Yang and F.Algregtsen,Fast computation of invariant geometric moments: A new method giving correct results,in Proc. IEEE Int. Conf. on Image Proc.,1994.
[41] Y. Rui,A. C. She,and T. S. Huang,Modified fourier descriptors for shape representation—a practical approach,in Proc. of First International Workshop on Image Databases and Multi Media Search,1996.
[42] A. Pentland,R. W. Picard,and S. Sclaroff, Photobook: Content-based manipulation of image databases,International Journal of Computer Vision,1996.
[43] G. C.-H. Chuang and C.-C. J. Kuo,Wavelet descriptor of planar curves: Theory and applications,IEEE Trans. Image Proc. 1996,5(1):56～70.
[44] http://www.vision.ee.ethz.ch/～rsia/.
[45] http://www.ntu.edu.sg/home/astimo/Research/Project/RS2I.htm.
[46] http://terraserver.microsoft.com/.
[47] Tom Barclay, Jim Gray, Don Slutz,Microsoft TerraServer: a spatial data warehouse,2000,29(2):307～318.
[48] http://coconut.al.umces.edu/Imagine84/Image-Catalog.pdf.
[49] http://www.lizardtech.com/.
[50] www.ermapper.com/.
[51] 李军．海量影像数据库的研究、设计与实现[博士论文]．武汉：中国地质大学，2000．
[52] 王兴玲．基于XML的地理信息Web服务研究[博士论文]．中科院遥感应用研究所，2002．
[53] 白玉琪．空间信息搜索引擎研究[博士论文]．北京：中国科学院遥感应用研究所．2003．
[54] R.Agrawal,C.Faloutsos,A.Swami,Efficient Similarity Search in Sequence Databases. FODO Conference,1993.
[55] C.Faloutsos,M.Ranganathan,Y.Manolopoulos,Fast Subsequence matching in Time-Series Databases. Proceedings of the 1994 ACM SIGMOD Conference,Minneapolis. 1994,5:419～429.
[56] 杨娜，罗航哉，等．基于内容的图像检索中相关反馈算法综述，计算机科学．2001，28(9)：105～111．
[57] Yong Rui,Thomas S. Huang,Michael Ortega, Sharad Mehrotra,"Relevance
Feedback: A Power Tool for Interactive Content-Based Image Retrieval," IEEE Trans. On Circuit and Video Technology,. Sep. 1998.
[58] H. Muller,W. Muller,D. M. Squire,S. Marchand-Maillet,and T. Pun. Performance evaluation in content-based image retrieval: Overview and proposals. Pattern Recognition Letters. 2001,22(5):593～601.
[59] 孙兴华．基于内容的图像检索研究[博士论文]．南京：南京理工大学，2001．
[60] P. W. Huang, S. K. Dai. Design of A Two-Stage Content-Based Image Retrieval System Using Texture Similarity. Information Processing and Management: an International Journal. 2004,40(1): 81～96.
[61] http://www-prima.inrialpes.fr/ECVNet/benchmarking.html.
[62] Zheng Chen,Liu Wenyin,Feng Zhang,Mingjing Li,HongJiang Zhang: Web mining for Web image retrieval. JASIST 2001,52(10):831～839.
[63] http://www.sbg.ac.at/geo/eogeo/authors/frew/frew.htm.
[64] BS Manjunath and WY Ma," Browsing Large Satellite and Aerial Photographs," in Proceedings of the Third IEEE International Conference on Image Processing, 1996,2:765～768.
[65] Bhagavathy, S.,S.Newsam,and BS Manjunath,"Modeling Object Classes in Aerial Images Using Texture Motifs," in Proceedings of the IAPR International Conference on Pattern Recognition, 2002,2:981～984.
[66] W. Y. Ma and B. S. Manjunath,"A texture thesaurus for browsing large aerial photographs," Journal of the American Society for Information Science (JASIS), 1998,49(7):633～648.
[67] B. S. Manjunath and W. Y. Ma,"Texture Features for browsing and retrieval of image data," Technical Report CIPR-TR-95-06,July 1995.
[68] D. Andresen,L. Carver,R. Dolin,C. Fischer,J.Frew,M. Goodchild,O.Ibarra,R. Kothuri,M. Larsgaard,B. Manjunath,D. Nebert,J. Simpson,T. Smith,T. Yang,and Q. Zheng. The WWW Prototype of the Alexandria Digital Library. Proceedings of ISDL'95: International Symposium on Digital Libraries,Aug. 1995.
[69] WY Ma and BS Manjunath,"Edge flow: a framework of boundary detection and image segmentation," Proc. IEEE International Conference on Computer Vision and Pattern Recognition(CVPR'97),San Juan,Puerto Rico,June 1997:744～749.
[70] Norbert Strobel,Sanjit K. Mitra,and BS Mmljunath. An Approach to Efficient Storage,Retrieval,and Browsing of Large scale Image Databases. Proceedings of the SPIE, 1995:324～335.
[71] WY Ma and BS Manjunath. A texture thesaurus for browsing large aerial photographs ",Journal of the American Society for Information Science,Wiley for ASIS, 1998,49(7):633～648.
[72] Chung-Sheng Li,Lawrence D. Bergman,Vittorio Castelli,John R. Smith: SPIRE: A Progressive Content-Based Spatial Image Retrieval Engine. SIGMOD Conference 2000: 598(Berkeley Digital Library Project),2000.
[73] Chad Carson,Megan Thomas, Serge Belongie,Joseph M. Hellerstein, and Jitendra Malik,Blobworld: A system for region-based image indexing and retrieval,Third Int. Conf. on Visual Information Systems,June 1999.
[74] http://elib.cs.berkeley.edu/photos/blobworld/.
[75] Julio Barros,James French,Worthy Martin,Patrick Kelly, and James M. White. Indexing multispeetral images for content-based retrieval. In Proceedings of the 23rd AIPR Workshop on Image and Information Systems: Applications and Opportunities,Washington,D.C.,October 1994.
[76] Julio Barros,James. French,Eorthy Martin, " System for Indexing Multi-spectral Satellite Images for Efficient Content-Based Retrieval," in Storage and Retrieval for Image and Video Databases Ⅲ,W. Niblack,R. C. Jain,editors,Proc. SPIE 2420,Feb. 1995:228～237.
[77] A. Vellaikal and C.-C. Jay Kuo,Content-based image retrieval using multiresolution histogram representation,SPIE's Photonics East,Philadelphia, PA, Oct. 1995:22～26.
[78] A. Vellaikal,Xia Wan and C.-C. Jay Kuo,Content-based retrieval of color and multispectral images,International Symposium on Communications,National Taiwan University, Taipei,TAIWAN,ROC,Dec. 1995:27～29.
[79] A.Vellaikal,C. Kuo,and S. Dao: "Content-based retrieval of remote sensed images using vector quantization",Proceedings of SPIE, 1995:178～189.
[80] A.Vellaikal,Son Dao and C.-C.Content-Based Retrieval of Remote Sensed Images Using a Feature-Based Approach,1995.
[81] A.Vellaikal,C.-C. Jay Kuo,Son Dao,Content-Based Retrieval of Color and Multispectral Images Using Joint Spatial-Spectral Indexing, SPIE,1995:232～243.
[82] A.Vellaikal,S. Dao and C.-C. Jay Kuo,Feature-based representation to aid content-based retrieval of remote sensed images,Science Information Management and Data Compression Workshop,Greenbelt,MD. Oct. 1995:26～27.
[83] A.Vellaikal,C.-C. Jay Kuo and S. Dao,Content-based retrieval and browsing of remote sensed images with VQ,SPIE's International Symposium on Aerospace/Defense Sensing and Dual-Use Photonics,Orlando,Florida,April 1995:17～21.
[84] A.Vellaikal and C.-C. Jay Kuo,Joint spatial-spectral indexing for image retrieval,IEEE International Conference on Image Processing,Lausanne,Switzerland, Sept. 1996:16～19.
[85] Manjunath BS and WY Ma,Texture features for browsing and retrieval of image data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996,18(8):837～842.
[86] G. Sheikholeslami,Wang,Wenjie; Zhang,Aidong. A Model of Image Representation and Indexing in Image Database Systems,July 20,1998.
[87] G.Sheikholeslami,A. Zhang and L. Bian," A Multi-Resolution Content-Based
Retrieval System for Geographical Images",GeoInformatica,An International Journal on Advances of Computer Science for Geographic Information Systems,1999,3(2):109-139.
[88] G. Sheikholeslami,A. Zhang,and L. Bian," Geographical Image Classification and Retrieval ",The Proceedings of the 5th ACM Workshop on Geographic Information Systems,Las Vegas,Nevada,November 1997:58～61.
[89] Chung-Sheng Li; Castelli,V.;Deriving texture feature set for content-based retrieval of satellite image database,ICIP(1), 1997:576～579.
[90] Lawrence D. Bergman,Vittorio Castelli,and Chung-Sheng Li,Progressive Content-Based Retrieval from Satellite Image Archives,D-Lib Magazine,October 1997.
[91] V.Castelli,L. D. Bergman,C.-S. Li,J. R. Smith and A.Thomasian. Progressive Content-based Retrieval of Satellite Image Database through Internet. IEEE Tyrrhenian Intern. Workshop on Digital Comm.,Ischia,Italy, Sept. 1998.
[92] H. Rehrauer and K. Seidel and M. Datcu,Multiscale Markov Random Fields for Large ImageDatasets Representation, In: IEEE Intemational Geoscience and Remote Sensing Symposium,IGARSS'97,1997.
[93] H.Rehrauer and K. Seidel and M. Datcu, Multiscale Image Segmentation with a Dynamic Label Tree,In: IEEE International Geoscience and Remote Sensing Symposium,IGARSS'98,1998.
[94] H.Rehrauer, K.Seidel,and M. Datcu,Bayesian Image Segmentation using a Dynamic Pyramidal Structure,Proceedings of the 18th International Workshop on Maximum Entropy and Bayesian Methods (MaxEnt'98) 1998.
[95] H.Rehrauer, K.Seidel and M. Datcu,"Multi-scale Indices for Content-based Image Retrieval",IEEE International Geoscience and Remote Sensing Symposium, IGARSS,99,1999.
[96] Hubert Rehrauer, Klaus Seidel,Mihai Datcu, Characteristic Scale Detection in Remote-sensing Data ,IGARSS'99,1999.
[97] K.Seidel and M. Schr?der and H. Rehrauer and M. Datcu,Meta Features for Remote Sensing Image Content Indexing,In: IEEE International Geoscience and Remote Sensing Symposium,IGARSS'98,1998.
[98] K.Seidel,M.Schr?der, H. Rehrauer,G.Schwarz and M. Datcu,"Query by Image Content from Remote Sensing Archives",IEEE International Geoscience and Remote Sensing Symposium (IGARSS'98), 1998:393～396.
[99] M. Datcu,K. Seidel,M. Schr?der, H. Rehrauer, C. Palubinskas and M. Walessa,"Image Information Mining and Remote Sensing Data Interpretation",IEEE International Geoscience and Remote Sensing Symposium (IGARSS2000),2000.
[100] M.Schr?der and K. Seidel and M. Datcu,Gibbs Random Field Models for Image Content Characterization,In: IEEE International Geoscience and Remote
Sensing Symposium,IGARSS'97,1997.
[101] M. Schr?der and K. Seidel and M. Datcu, User-oriented Content Labeling in Remote Sensing Image Archives,In: IEEE International Geoscience and Remote Sensing Symposium,IGARSS'98,1998.
[102] M. Datcu,K. Seidel,and M. Walessa, Spatial information retrieval from remote sensing images-Part Ⅰ: Information theoretical perspective,In: IEEE Trans. on Geoscience and Remote Sensing, 1998,36(5): 1431～1445.
[103] M. Schr?der, H. Rehrauer, K. Seidel and M. Datcu,Spatial information retrieval from remote sensing images-Part Ⅱ: Gibbs Markov Random Fields,in: IEEE Trans. on Geoscience and Remote Sensing, 1998,36(5): 1446～1455.
[104] M. Schr?der, K. Seidel,and M. Datcu,Bayesian labeling of remote sensing image content,Proceedings of the 18th International Workshop on Maximum Entropy and Bayesian Methods (MaxEnt'98), 1998.
[105] M. Schr?der and A. Dimai,Texture Information in Remote Sensing Images: A Case Study, Workshop on Texture Analysis (WTA'98),Freiburg,1998.
[106] P.Agouris,J. Carswell,A. Stefanidis: An Environment for Content-Based Image Retrieval from Large Spatial Databases,ISPRS Journal of Photogrammetry and Remote Sensing,Elsevier, 1999,54(4):263～272.
[107] P.Agouris.,A. Stefanidis & J. Carswell "Intelligent Retrieval of Digital Images from Large Geospatial Databases",International Archives of Photogrammetry and Remote Sensing,Vol. ⅩⅩⅫ,Part 3/1, 1998:515～522.
[108] P.Agouris and A. Stefanidis.Intelligent Image Retrieval from Large Databases Using Shape and Topology, IEEE International Conference on Image Processing (ICIP) '98,Vol. 2,pp. Ⅱ779-Ⅱ783,Chicago,Oct. 1998.
[109] Qimin CHENG; Chongjun YANG, Zhenfeng SHAO, Feixiang CHEN Application of M-Band Wavelet Theory to Texture Analysis in Content-Based Aerial Image Retrieval, IGARSS'04.
[110] B.Raghunathan and S.T. Acton,Content based retrieval for remotely sensed imagery, Proc. IEEE Southwest Symposium on Image Analysis and Interpretation,Austin,Texas,April,2000.
[111] Luis M. del Val Cura,Neucimar Jer?nimo Leite and Claudia Bauzer Medeiros,"An Architecture for Content-Based Retrieval of Remote Sensing Images".IEEE International Conference on Multimedia and Expo (Ⅰ) ,2000:303～306.
[112] Irwin E. Alber, Ziyou Xiong,Nancy Yeager, Morton Farber and William M. Pottenger. "Fast Retrieval of Multi-and Hyperspectral Images Using Relevance Feedback". Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS 2001). July 2001,3:1149～1151.
[113] Irwin E. Alber, Morton Farber, Nancy Yeager, Ziyou Xiong,and William M. Pottenger. "Retrieval of Multi-and Hyperspectral Images Using an Interactive
Relevance Feedback form of Content-based Image Retrieval". Proceedings of the15th Annual International Symposium on Aerospace/Defense Sensing, Simulation,and Controls,Orlando,FL,April 2001:4384～4388.
[114] Luo Rui,Zhang Yongsheng,Fan Yonghong,Deng Xueqing,Research on content-based remote sensing image retrieval: the strategy for visual feature selection,extraction,description and similarity measurement,Info-tech and Info-net,2001. Proceedings. ICⅡ 2001-Beijing. 2001 International Conferences on, 2001,1:21～325.
[115] O. Kao,T. Bretschneider, GR. Joubert,Image Retrieval with Gabor-Wavelet-Networks,Proceedings of the International Conference on Internet and Multimedia Systems and Applications,2002:312～317.
[116] O. Kao,I. la Tendresse,CLIMS-A system for image retrieval by using colour and wavelet features,Advances in Information Systems,Proceedings of the First Biennial International Conference on Advances in Information Systems (ADVIS'2000),Lecture Notes in Computer Science,LNCS 1909,Springer Verlag, 2000:238～248.
[117] O. Kao,I. la Tendresse,On the Impact of the Number of Coefficients on the Quality of Wavelet-based Image Retrieval,Proceedings of the 2001 International Conference on Imaging Science,Systems,and Technology,2001:361～372.
[118] O. Kao,I. la Tendresse,Dynamic Image Retrieval with Wavelet Coefficients,Proceedings of the 2002 International Conference on Imaging Science,Systems,and Technology, CSREA Press, 2002,2:477～480.
[119] O. Kao,T. Bretschneider, GR. Joubert,Image retrieval with Gabor-Wavelet-Networks,Proceedings of the 6th International Conference on Internet and Multimedia Systems and Applications (IMSA 2002), 2002:312～317.
[120] T. Bretschneider, O. Kao,Retrieval of Multispectral Satellite Imagery on Cluster Architectures,Proceedings of the EuroPar, Lecture Notes in Computer Science, 2002:342～346.
[121] T. Bretschneider, O. Kao,A Retrieval system for remotely sensed imagery, Proceedings of the 2002 International Conference on Imaging Science,Systems,and Technology,2002: 439～445.
[122] T.Bretschneider, R. Cavet and O. Kao,"Retrieval of remotely sensed imagery using spectral information content",Proceedings of the International Geoscience and Remote Sensing Symposium, 2002,4:2253～2256.
[123] 吴均．基于内容的图像检索技术及其在遥感图像中的应用研究[博士论文]．北京：中国科学院遥感应用研究所，2001．
[124] 周焰，李德仁，徐长勇．基于形状的遥感图像检索系统，华中科技大学学报(自然科学版)，2003，31(3)：14～16．
[125] 周焰．遥感图像内容查询若干问题的研究[博士后出站报告]．武汉：武汉大学，2003．
[126] JR Smith and SF Chang,Automated Binary Texture Feature Sets for Image Retrieval,International Conference on Acoustics, Speech,and Signal Processing (ICASSP),1996.
[127] 张继贤，李德仁．影象纹理的多尺度分析，环境遥感．1996，11(1)：1～13．
[128] 陈武凡．小波分析及其在图像处理中的应用．北京：科学出版社，2002．
[129] M.K. Mandal and T. Aboulnasr,"Fast Wavelet Histogram Techniques for Image Indexing", Computer Vision and Image Understanding, July/August, 1999, 75(1/2):99～110.
[130] 程起敏，杨崇俊，邵振峰．基于小波变换的遥感影像渐进式检索，中国图象图形学报，Vol．8(A)，Spec．2003：413～417．
[131] 秦前清，杨宗凯．实用小波分析．西安：西安电子科技大学出版社，1994．
[132] http://sipi.usc.edu/services/database/database.cgi?volume=aerials.
[133] 朱长青．小波分析理论与影像分析．北京：测绘出版社，1998．
[134] 王智均，李德仁，李清泉．多进制小波理论在SPOT和TM影像融合中的应用，武汉大学学报．信息科学版．2000，26(1)：24～28．
[135] 唐向宏，谢书琴，李奇良，等．M 带小波变换在图像压缩中的应用，电路与系统学报．2003，7(1)：108～111．
[136] Chitre,Y and A.P. Dhawan,"M-Band wavelet discrimination of natural textures",Pattern Recognition, 1999,32(5):773～789.
[137] M. Acharyya and M.K. Kundu,"An adaptive approach to unsupervised texture segmentation using M-Band wavelet transform",Signal Processing, 2001,81:1337～1356.
[138] M. Kokare,B. N. Chatterji and P. K. Biswas ,"M-Band Wavelet Based Texture Features For Content Based Image Retrievar",Electronics and Electrical Communication Engineering Department,ICVGIP2002,Dec. 2002.
[139] 程起敏，杨崇俊，邵振峰．基于多进制小波变换的渐进式纹理图像检索．武汉大学学报．信息科学版(已录用)．
[140] P.W. Huang,S. K. Dai. "Design of A Two-Stage Content-Based Image Retrieval System Using Texture Similarity". Information Processing & Management,2004,40(1):81～96.
[141] E.Remias and G. Sheikholeslami and A. Zhang," Block-Oriented Image Decomposition and Retrieval in Image Database Systems," in the 1996 International Workshop on Multi-media Database Management Systems,Blue Mountain Lake,New York,August, 1996.
[142] 章毓晋．基于内容的视觉信息检索．北京：科学出版社，2003．
[143] 李爽，丁圣彦，等．遥感影像分类方法比较研究，河南大学学报．自然科学版．2002，32(2)：70～73．
[144] 杨翔英，章毓晋．小波轮廓描述及在图像查询中的应用，计算机学报，1999，22(7)：752～757．
[145] K. M?ller, J.R. Ohm: "Wavelet-based contour descriptor," MPEG-7 proposal no. P567,Jan. 1999.
[146] 袁野，欧宗瑛．基于小波变换和模糊算法医学图像边缘检测算法．大连理工大学学报，2002．42(4)．
[147] 姚玉荣，章毓晋．利用小波和矩进行基于形状的图象检索．中国图象图形学报，2000．5(3)．
[148] 薛鸿民，刘志镜，刘利等．基于形状的图像检索的关键技术研究．计算机应用研究2002，19(11)：61～64．
[149] 刘继敏，史忠植．一种基于形状的图像信息检索方法，软件学报．2000，11(1)：110～115．
[150] 袁华，董守斌，张凌，等．GFO和不变矩实现基于形状的图像检索新方法，华南理工大学学报(自然科学版)，2002，30(4)．
[151] 黎夏．形状信息的提取与计算机自动分类．环境遥感，1995，10(4)．
[152] 杨述斌．图象边缘检测技术概述．武汉化工学院学报．2003，25(1)：73～76．
[153] Hu M K. Visual Pattern Recognition by Moment Invariants,IRE Trans. Inform. Theory, 1962,IT-8:179～187.
[154] Chen C C. Improved moment invariants for shape discrimination.Pattern Recognition, 1993,26(5):683～686.
[155] 王波涛，孙景鳌，等．相对矩及在几何形状识别中的应用，中国图象图形学报．A辑．2001，6(3)：296～300．
[156] 边肇祺，模式识别，北京：清华大学出版社，1998．
[157] Mallat S,Hwang W L.,Singularity detection and processing with wavelets,IEEE IT,3,1992,8(2):617～643.
[158] M. Do,So Ayer and M. Vetterli,"Invariant image retrieval using wavelet maxima moment",Proc. Visual '99,Amsterdam,1999.
[159] M.Trimeche,F. Alaya Cheikh and M.Gabbouj,"Similarity Retrieval of Occluded Shapes Using Wavelet-Based Shape Feature",submitted to the SPIE International Symposium on Internet Multimedia Management Systems (VV10),Boston,Massachusetts,USA,November 5-8,2000.
[160] F. Alaya Cheikh,A. Quddus and Moncef Gabbouj,"Multi-level Shape Recognition based on Wavelet-Transform Modulus Maxima," Proc. of Southwest Symposium on Image Analysis and Interpretation,SSIAI 2000,Austin,Texas,USA,April 2-4,2000:8～12.
[161] F.Alaya Cheikh,A.Quddus and M.Gabbouj,"Contour-based Object Recognition using Wavelet-Transform",European Signal Processing Conference,Eusipco-2000,Tampere,Finland,September 5-8,2000.
[162] F. Alaya Cheikh,A. Quddus and M. Gabbouj,"Shape Recognition based on Wavelet-Transform Modulus Maxima", 7th IEEE International Conference on Electronics,Circuits and Systems (ICECS2K),pp.
461-464,Beirut,Lebanon,December 17-19,2000.
[163] A.Quddus,EAlaya Cheikh and M.Gabbouj,"Wavelet-based Multi-level Object Retrieval In Contour Images",Proc. of the International Workshop on Very Low Bit Rate Video Coding (VLBV'99),Kyoto,Japan,October 29-30,1999.
[164] 李德仁，陈晓勇．用数学形态学变换自动生成DTM三角形格网的方法测绘学报．1990,19(3)：161～172．
[165] 戴青云，余英林．数学形态学在图象处理中的应用进展．控制理论与应用．2001，18(4)：478～482．
[166] 王宇，王乘，刘吉平．一种基于数学形态学的遥感图像边缘检测算法，重庆邮电学院学报．自然科学版．2003，15(2)：57～60．
[167] BibRef. Lee, JS,Haralick, RM, Shapiro, LG, Morphologic Edge Detection, J-RA(3), 1987.
[168] 杨述斌，彭复员，等．基于形态学的遥感图像全方位边缘检测算法研究．遥感信息．2003，1：2～3，47．
[169] 朱晓亮，彭复员，等．基于多尺度形态学的弱目标图像处理方法．红外与激光工程．2002．31(6)：482～484．
[170] M. K. Kundu,B. Chanda and Y. V. Padmaja,"Morphologic edge detection with multi scale approach",in Proc.IEEE Int. Conf. TENCON'98, New Delhi,India, 1998:53～56.
[171] Bhabatosh Chanda, Malay K. Kundu And Y. Vani Padmaja, A MULTI-SCALE MORPHOLOGIC EDGE DETECTOR,Pattern Recognition, October, 1998,31(10):1469～1478.
[172] 崔新春，裘季冰，等．基于小波变换和数学形态学的人造景物提取．上海师范大学学报．自然科学版．2002，31(1)：42～46．
[173] 陶洪久，柳健，等．基于小波变换和数学形态学的遥感图像边缘检测．红外与激光工程．2002，31(2)：154～157．
[174] 张艳宁，郑江滨，王晓红，等．一种有效的遥感图像目标识别方法．信号处理，2002，18(1)．
[175] 刘循，游志胜，多尺度形态学图像边缘检测方法光电工程．2003，30(3)：56～58
[176] 周维忠，孙国基，基于多尺度数学形态学的边缘检测数据．采集与处理．2000，15(3)：316～319．
[177] 吕铁英，彭嘉雄．一种基于数学形态学的图象多尺度分析方法的研究．数据采集与处理．1998，13(2)：107～111．
[178] 冯玉才，曹奎，等．一种支持快速相似检索的多维索引结构．软件学报．2002，13(8)：1678～1685．
[179] 刘芳洁，董道国，薛向阳．度量空间中高维索引结构回顾．计算机科学．2003，30(7)：64～68．
[180] 董道国，薛向阳，罗航哉．多维数据索引结构回顾．计算机科学，2002，29(3)：1～6
[181] 梅承力，周源华．高维数据空间索引的研究．红外与激光工
程．2002，31(1)：77～81．
[182] 刘传才．基于可视特征度量距离的图像检索．福州大学学报(自然科学版)．2001，29(2)：12～15．
[183] Bozkaya T. Ozsoyoglu M. Distance-based indexing for high-dimensional metric spaces. ACM SIGMOD Intl. Conf. On Management of Data,Sigmod Record, 1997,26(2):357～368.
[184] A.Guttman. R-trees: a dynamic index structure for spatial searching. In ACM SIGMOD International Conference on Management of Data,pages 47～57,Boston,MA,June 1984.
[185] T. Sellis,N. Roussopoulos,and C. Faloutsos,"The R + -tree: A dynamic index for multidimensional objects," in Proceedings of Very Large Data Bases, Brighton,England, 1987:3～11.
[186] N. Beckmann,H.-P. Kriegel,R. Schneider, and B. Seeger, The R*-tree: An efficient and robust access method for points and rectangles,Proceedings of ACM SIGMOD Int'l. Conf. on Management of Data, 1990:322～331.
[187] D. A. White and R. Jain. Similarity indexing with the SS-tree. In International Conference on Data Engineering (ICDE),New Orleans,LA,Mareh 1996:516～523.
[188] Berchtold S.,Keim DA,Kriegel HP, The X-Tree: An Index Structure for High-Dimensional Data,Proc. 22th Int. Conf. on Very Large Data Bases,Bombay, India, 1996.
[189] N. Katayama and S. Satoh,The sr-tree: An index structure for high-dimensional nearest neighbor queries,Proceedings of ACM SIGMOD,May 1997.
[190] Nievergelt,J.,H. Hinterberger, and K. Sevcik,The grid file: An adaptable,symmetric multikey file structure,In A. Duijvestijn and p. Lockemann (Eds.),Proc. 3 rd ECI Conf.,Number 123 in LNCS,Berlin/Heidelberg/New York, Springer-Verlag, 1981:236～251.
[191] Markku Tamminen: The Extendible Cell Method for Closest Point Problems. BIT 22(1), 1982:27～41.
[192] K. Hinrichs: Implementation of the Grid File: Design,Concepts and Experience,BIT 25,1985:569～592.
[193] A. Hutflesz,HW Six,P. Widmayer: The twin grid file: a nearly space optimal index structure. Proc. Int. Conf. on Extending Database Technology,1988.
[194] J. L.Bentley, Multidimensional Binary Search Trees Used for Associative Searching,Communications of the ACM 19, 1979.
[195] JT Robinson,The KDB-Tree: A Search Structure for Large Multidimensional Dynamic Indexes,Proc. SIGMOD ACM, 1981:10～18.
[196] Raphael A. Finkel and Jon Louis Bentley. Quad trees: A data structure for retrieval on composite keys. Acta Informatica4,1974:1～9.
[197] H. Samet, 1984,The Quadtree and Related Hierarchical Data
Structures,Computing Surveys 16,187.
[198] Roger Weber, Hans-J?rg Schek,Stephen Blott,A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces,Proceedings of the 24rd International Conference on Very Large Data Bases, 1998:194～205.
[199] Y.Sakurai,M.Yoshikawa,S.Uemura and H. Kojima, The A-tree: An Index Structure for High-Dimensional Spaces Using Relative Approximation,Proc. 26th Int. Conf. on Very Large Data Bases (VLDB2000), 2000:516～526.
[200] C. Faloutsos and K.-I.(David) Lin,Fastmap: A fast alogorithm for indexing,data-mining and visualization of traditional and multimedia datasets,in Proc. of SIGMOD,1995:163～174.
[201] R. Ng and A. Sedighian,Evaluating multi-dimensional indexing structures for images transformed by principal component analysis,in Proc. SPIE Storage and Retrieval for Image and Video Databases,1996.
[202] S.Chandrasekaran,B. S. Manjunath,Y. F. Wang,J. Winkeler, and H. Zhang,An eigenspace update algorithm for image analysis,CVGIP: Graphical Models and Image Processing Journal,1997.
[203] Burkhard,W.A.; Keller, R.M.; Some approaches to Best-Match File Searching, Comm. ACM.,16,4. 1973.
[204] Baeza-Yates R, Cunto W, Manber U, Wu, S, Proximity matching using fixed-queries trees, In: Proc. 5th Combinatorial Pattern Matching (CPM'94), LNCS 807,1994:198～212.
[205] Baeza-Yates R. Searching: an algorithmic tour. Encyclopedia of computer science and technology, Marcel Dekker Inc. 1997,37:331～359.
[206] Chavez E,Marroquin J,Navarro G. Overcoming the curse of dimensionality. European Workshop on Content-based multimedia Indexing (CBMI'99),1999:57～64.
[207] Uhlmann J. Satisfying general proximity/similarity queries with metric trees. Information Processing Letters, 1991,40:175～179.
[208] Yianlios Y. Locally lifting the curse of dimensionality for nearest neighbor search. DEMACS Implementation Challenge,ALENEX'99,Baltimore,MD,1999.
[209] Kalantari I,McDonald G. A data structure and an algorithm for the nearest point problem. IEEE Transactions on Software Engineering, 1983.
[210] Brin S. Near neighbor search in large metric spaces. Proc. 21st Conf. On Very Large Database (VLDB'95), 1995:574～584.
[211] Dehne F, Nolteimer H, Voronoi trees and clustering problems, Information Systems, 1987,12(2): 171～175.
[212] P.Ciaccia,M. Patella,P. Zezula; M-Tree: An Efficient Access Method for Similarity Search in Metric Spaces; Proc. of the 23rd VLDB Conference, 1997:426～435.
[213] G. Navarro; Searching in metric spaces by spatial approximation; In: Proc. String Processing and Information Retrieval(SPIRE'99),IEEE CS Press,1999:141～148.
[214] E V Ruiz,An algorithm for finding nearest neighbours in (approximately) constant average time, Pattern Recognition Letters, 1986,4(3): 145～157.
[215] Maria Luisa Mic?,Jos? Oncina, Enrique Vidal,A new version of the nearest-neighbour approximating and eliminating search algorithm (AESA) with linear preprocessing time and memory requirements,Pattern Recognition Letters, 1994,5(1):9～17.
[216] T. Bozkaya and M. Ozsoyoglu. Indexing large metric spaces for similarity search queries. ACM Transaction on Database Systems, 1999,24(3):361～404.
[217] Ada Wai-Chee Fu,Polly Mei-shuen Chan,Yin-Ling Cheung, and Yiu Sang Moon. Dynamic vp-tree indexing for n-nearest neighbor search given pair-wise distances. VLDB Joumal, 2000,9(2): 154～173.
[218] Xiangmin Zhou,Guoren Wang,Jeffrey Xu Yu,Ge Yu: M+-tree: A New Dynamical Multidimensional Index for Metric Spaces. ADC 2003:161～168.
[219] Rui Mao,Wenguo Liu,et. On index methods for an image database. MMDB'03. (submitted).
[220] Neha Singh,Analysis of search algorithms and tree structures for proximity search in metric spaces,Undergraduate Honors Thesis,Department of Computer Sciences,The University of Texas at Austin,Fall 2002.
[221] Edgar Ch?vez,Gonzalo Navarro, Ricardo Baeza-Yates, Jos? Luis Marroqu?n, Searching in metric spaces, ACM Computing Surveys (CSUR), 2001,33(3):273～321.
[222] 王小平，曹立明．遗传算法——理论、应用与软件实现．西安：西安交通大学出版社．2002．
[223] 陈国良，王煦法，庄镇泉，等．遗传算法及其应用．重庆：人民邮电出版社．2001．
[224] 张笃振，李一民．基于遗传算法的彩色图象分割．昆明理工大学学报(理工版)，2003，28(4)：57～59．
[225] 施建强，刘晓平．基于遗传算法的数据挖掘技术的研究．电脑与信息技术，2003．1：9～15．
[226] 刘勇国，李学明，张伟，等．基于遗传算法的特征子集选择．计算机工程，2003，29(6)：19～21．
[227] 吴佳英，郑金华，遗传算法的研究与发展动向．衡阳师范学院学报(自然科学)，2003，3(3)：30～33．
[228] 徐丽娜，邓正隆，遗传算法与最优化，黑龙江自动化技术与应用，1996，15(4)：1～4．
[229] 邵红，崔文成，张健武等，遗传算法在基于内容的图像检索中的应用．计算机工程，2003．29(16)：21～22．
[230] 翟宜峰，李鸿雁，刘寒冰等，用遗传算法优化神经网络初始权重的方法．吉林大学学报(工学版)，2003，33(2)：45～50．
[231] 李军，费川云．地球空间数据集成研究概况．地理科学进展，2000．
[232] 方艳梅，杨灿，等．支持基于内容检索的图像数据模型的设计．计算机应用．2001，21(7)：23～26．
[233] 葛咏，郭大海．基于Clinet/Server的影像数据库管理模式．计算机工程与应用．2000，36(5)：129～130，189。
[234] 王密，龚健雅．基于扩展关系数据库的遥感影像数据库管理系统的研究与实现．测绘信息与工程．2002，27(5)：1～3．
[235] 罗睿，张永和，等．遥感图像数据库基于内容查询的研究．遥感学报，2002，6(1)：24～29．
[236] 罗睿，张永生，李颖，等．遥感影像数据模型及影像数据库的建立．测绘学院学报，2000，17(4)：273～276．
[237] 周学海，李光亚．按内容检索的图象数据库系统数据模型．软件学报．1998，9(3)：186～189．
[238] 陈跃峰，肖自美．基于内容查询的图象数据库系统模型．中国图象图形学报．A辑．1997，2(8)：634～637．
[239] 王密．大型无缝影像数据库系统(GeoImageDB)的研制与可量测虚拟现实(MVR)的可行性研究[博士论文]．武汉：武汉测绘科技大学，2001．
[240] 虞万荣，张银福，等．图像数据库Web检索接口的设计与实现，微型电脑应用．2002，18(3)：24～25，9．
[241] 万志坚，李文锋．Web环境下基于内容查询的图像数据库研究．计算机工程与应用．2002，38(11)：207～208，215．
[242] 李文锋，万志坚．基于Web的图像数据库系统的设计与实现．计算机应用研究．2002，19(5)：82～83，94．
[243] 葛艳红，李文锋，万志坚．利用ISAPI,Java开发B/S模式的图像数据库．武汉理工大学学报．交通科学与工程版．2003，27(3)：388～390．
[244] 章毓晋．WWW上基于内容的图象检索系统．电子技术应用．1999，25(12)：11～13．
[245] Hongjiang Zhang, Liu Wenyin,Chunhui Hu,"iFind—A System for Semantics and Feature Based Image Retrieval over Internet",ACM MULTIMEDIA 2000--The 8th ACM International Multimedia Conference,Los Angeles,California,October 30-November 3,2000.
[246] G.B. Marchisio,J. Cornelison: "Content-based search and clustering of remotely sensing imagery",Proceedings of the IEEE International Conference on Geoscience and Remote Sensing, 1999,1:290～292.
[247] G.B. Marchisio,J. Cornelison: "Content-based search and clustering of remotely sensing imagery",Proceedings of the IEEE International Conference on Geoscience and Remote Sensing, 1999,1:290～292.
[248] GB. Marchisio,WH Li,M. Sannella, and JR Goldschneider,"GeoBrowser:An integrated environment for satellite image retrieval and mining",Proceedings of the IEEE International Geoscience and Remote Sensing Symposium,1998,2:669～673.
[249] Chung-Sheng Li,J. J. Turek,and E. Feig. Progressive template matching for content-based retrieval in earch observing satellite image databases. Proc. of SPIE Photonics East,November 1995.
[250] Chung-Sheng Li,John Turek: Content-Based Indexing of Earth-Observing Satellite Image Database with Fuzzy Attributes. Storage and Retrieval for Image and Video Databases(SPIE),1996:438～449.
[251] D.G Healey, A. Jain. Retrieving multispectral satellite images using physics-based invariant representations, IEEE Transactions on Pattem Analysis and Machine Intelligence, 1996,18(8):842～848.
[252] T. Smith. A digital library for geographically referenced materials. IEEE Computer,29(5): 54～60,1996.
[253] Marshall C. Ramsey ,Hsinchun Chen,Bin Zhu,Bruce R. Schatz,A collection of visual thesauri for browsing large collections of geographic images,Joumal of the American Society for Information Science,1999,50(9):826～834.
[254] Wu,P.,Manjunath,BS,Newsam,SD, Shin,HD,A texture descriptor for browsing and similarity retrieval,SP:IC(16),2000,1-2:33～43.
[255] Zhu,B.,Ramsey, M.,Chen,H.,Creating a Large-Scale Content-Based Airphoto Image Digital Library, IP(9),2000,1:163～163.
[256] Smeulders A,Worring M,Santini S,Gupta A, and Jain R, Content based image retrieval at the end of the early years, IEEE Trans PAMI, 2000,22(12):1349～1380.
[257] 陈剑赟，老松扬，吴玲达．基于内容的图像检索的发展最新趋势．计算机工程与应用，2002，138(10)：47～49．
[258] 胡著智，王慧麟，陈钦峦．遥感技术与地学应用．南京：南京大学出版社，1999．
[259] 贾永红．计算机图像处理与分析．武汉：武汉大学出版社，2001．
[260] 程起敏，杨崇俊，邵振峰．基于内容的图像检索技术研究及前景．武汉大学学报．信息科学版(增刊)．2003．3．
[261] 王文惠．基于内容的图像检索技术的研究和发展．计算机工程与应用，2001．
[262] 张永生．遥感图象信息系统．北京：科学出版社，2000．
[263] 朱述龙，张占睦．遥感图象获取与分析．北京：科学出版社，2000．
[2] 路甬祥．合作开发数字地球，共享全球数据资源，"Towards Digital Earth",1999：3～5．
[3] 赵霈生．Internet GIS领域软件体系结构研究[博士论文]．中科院遥感应用研究所，2000．
[4] 杨崇俊．数字地球研究与中国对策．2000高技术发展报告，2000：152～156
[5] 杨崇俊．在互联网络上能找到地理空间数据吗?陈述彭．主编．数字地球百问，北京：科学出版社，1999：176～177．
[6] Y.Rui, T.S.Huang and Shih-Fu Chang. Image retrieval: current techniques, promising directions, and open issues, Journal of Visual Communication and Image Representation, 1999.10:39～62.
[7] ISO/IEC JTC 1/SC 29/WG11/N4063, MPEG-7 Multimedia Description Schemes XM (Version 10), 2001.
[8] http://farside.gsfc.nasa.gov/ISTO/DLT/NSF_pr.html.
[9] W.Niblack, R.Barber et al. The QBIC project: Querying images by content using color, texture and shape, in Proc. SPIE Storage and Retrieval for Image and Video Databases, 1994.
[10] M.Flickner, H.Sawhney, W.Niblack et al.Query by image and video content: The QBIC system, IEEE Computer, 1995.
[11] W.Equitz and W.Niblack, Retrieving Images from a Database using Texture--Alogrithms from the QBIC System, Technical Report RJ9805, Computer Science, IBM Research Report, 1994.
[12] J.R.Bach, C.Fuller, A.Gupta, et al. The Virage image search engine: An open framework for image management, in Proc.SPIE Storage and Retrieval for Image and Video Databases. 1996.
[13] A.Gupta and R.Jain, Visual information retrieval, Communications of the ACM. 40(5), 1997.
[14] http://vrw/excalib.com/cgi-bin/sdk/cst/cst2.bat.
[15] J.R.Smith and S.-F.Chang, Querying by color regions using the VisualSEEk content-based visual querysystem (M.T.Maybury, Ed.), in Intelligent Multimedia Information Retrieval, 1996.
[16] J.R.Smith and S.-F.Chang, Visualseek: A fully automated content-based image query system, in Proc. ACM Multimedia96,1996.
[17] J.R.Smith and S.-F.Chang, Visually searching the web for content, IEEE Multimedia Magazine, 1997, 4(3): 12～20.
[18] T.S.Huang, S.Mehrotra, and K.Ramachandran, Multimedia analysis and retrieval system (MARS) project, in Proc.of 33rd Annual Clinicon Library Application of Data Processing Digital Image Access and Retrieval, 1996.
[19] S.Mehrotra, K.Chakrabarti, M.Ortega, Y.Rui and T.S.Huang, Multimedia analysis and retrieval system, in Proc.of the 3rd Int. Workshop on Information Retrieval Systems, 1997.
[20] http://elib.cs.berkeley.edu/.
[21] http://public.lanl.gov/kelly/CANDID/index.shtml.
[22] http://www.alexandria.ucsb.edu/.
[23] http://www.intsci.ac.cn/image/mires.html.
[24] M.Swain and D.Ballard, Color indexing, International Journal of Computer Vision 7(1), 1991.
[25] M.Stricke rand M.Orengo,Similarity of color images,in Proc. SPIE Storage and Retrieval forImage and Video Databases, 1995.
[26] J.R.Smith and S.-F.Chang,Single color extraction and image query, in Proc. IEEE Int. Conf. on Image Proe.,1995.
[27] J.R.Smith and S.-F.Chang,Tools and techniques for color image retrieval,in IS&T/SPIE Proceedings,Vol.2670,Storage & Retrieval for Image and Video Databases Ⅳ, 1995.
[28] R. M. Haraliek,K. Shanmugam,and I. Dinstein,Texture features for image classification,IEEE Trans. On Sys. Man. and Cyb. SMC-3(6),1973.
[29] H. Tamura,S. Mori,and T. Yamawaki,Texture features corresponding to visual perception,IEEE Trans. On Sys.,Man. and Cyb. SMC-8(6),1978.
[30] G C. Cross and A. K. Jain,Markov random field texture models,IEEE Trans. Part. Recog. and Mach. Intell. 1983,5:25～39.
[31] A. R Pentland,Fractal-based description of natural scenes,IEEE Trans. Patt. Recog. and Mach. Intell. 1984 6(6):661～674.
[32] J. R.Smith and S.-F. Chang,Automated binary texture feature sets for image retrieval,in Proc. ICASSP-96,Atlanta,GA, 1996.
[33] T. Chang and C.-C. J. Kuo,Texture analysis and classification with tree-structured wavelet transform,IEEE Trans. Image Proc. 1993,2(4):429～441.
[34] A. Laine and J. Fan,Texture Classification by wavelet packet signatures,IEEE Trans. Part. Recog. and Mach. Intell. 1993,15(11): 1186～1191.
[35] M. H. Gross,R. Koch,L. Lippert,and A. Dreger, Multiscale image texture analysis in wavelet spaces,in Proc. IEEE Int. Conf. on Image Proc., 1994.
[36] A. Kundu and J.-L. Chen, Texture classification using qmf bank-based subband decomposition,CVGIP: Graphical Models and Image Processing. 1992,54(5):369～384.
[37] J. R. Smith and S. F. Chang,Transform features for texture classification and
discrimination in large image databases,in Proc. IEEE Int. Conf. on Image Proc., 1994.
[38] K. S.Thyagarajan,T. Nguyen,and C. Persons,A maximum likelihood aproach to texture classification using wavelet transform,in Proc. IEEE Int. Conf. on Image Proc., 1994.
[39] M. K. Hu,Visual pattern recognition by moment invariants,computer methods in image analysis,IRETransactions on Information Theory 8,1962.
[40] L. Yang and F.Algregtsen,Fast computation of invariant geometric moments: A new method giving correct results,in Proc. IEEE Int. Conf. on Image Proc.,1994.
[41] Y. Rui,A. C. She,and T. S. Huang,Modified fourier descriptors for shape representation—a practical approach,in Proc. of First International Workshop on Image Databases and Multi Media Search,1996.
[42] A. Pentland,R. W. Picard,and S. Sclaroff, Photobook: Content-based manipulation of image databases,International Journal of Computer Vision,1996.
[43] G. C.-H. Chuang and C.-C. J. Kuo,Wavelet descriptor of planar curves: Theory and applications,IEEE Trans. Image Proc. 1996,5(1):56～70.
[44] http://www.vision.ee.ethz.ch/～rsia/.
[45] http://www.ntu.edu.sg/home/astimo/Research/Project/RS2I.htm.
[46] http://terraserver.microsoft.com/.
[47] Tom Barclay, Jim Gray, Don Slutz,Microsoft TerraServer: a spatial data warehouse,2000,29(2):307～318.
[48] http://coconut.al.umces.edu/Imagine84/Image-Catalog.pdf.
[49] http://www.lizardtech.com/.
[50] www.ermapper.com/.
[51] 李军．海量影像数据库的研究、设计与实现[博士论文]．武汉：中国地质大学，2000．
[52] 王兴玲．基于XML的地理信息Web服务研究[博士论文]．中科院遥感应用研究所，2002．
[53] 白玉琪．空间信息搜索引擎研究[博士论文]．北京：中国科学院遥感应用研究所．2003．
[54] R.Agrawal,C.Faloutsos,A.Swami,Efficient Similarity Search in Sequence Databases. FODO Conference,1993.
[55] C.Faloutsos,M.Ranganathan,Y.Manolopoulos,Fast Subsequence matching in Time-Series Databases. Proceedings of the 1994 ACM SIGMOD Conference,Minneapolis. 1994,5:419～429.
[56] 杨娜，罗航哉，等．基于内容的图像检索中相关反馈算法综述，计算机科学．2001，28(9)：105～111．
[57] Yong Rui,Thomas S. Huang,Michael Ortega, Sharad Mehrotra,"Relevance
Feedback: A Power Tool for Interactive Content-Based Image Retrieval," IEEE Trans. On Circuit and Video Technology,. Sep. 1998.
[58] H. Muller,W. Muller,D. M. Squire,S. Marchand-Maillet,and T. Pun. Performance evaluation in content-based image retrieval: Overview and proposals. Pattern Recognition Letters. 2001,22(5):593～601.
[59] 孙兴华．基于内容的图像检索研究[博士论文]．南京：南京理工大学，2001．
[60] P. W. Huang, S. K. Dai. Design of A Two-Stage Content-Based Image Retrieval System Using Texture Similarity. Information Processing and Management: an International Journal. 2004,40(1): 81～96.
[61] http://www-prima.inrialpes.fr/ECVNet/benchmarking.html.
[62] Zheng Chen,Liu Wenyin,Feng Zhang,Mingjing Li,HongJiang Zhang: Web mining for Web image retrieval. JASIST 2001,52(10):831～839.
[63] http://www.sbg.ac.at/geo/eogeo/authors/frew/frew.htm.
[64] BS Manjunath and WY Ma," Browsing Large Satellite and Aerial Photographs," in Proceedings of the Third IEEE International Conference on Image Processing, 1996,2:765～768.
[65] Bhagavathy, S.,S.Newsam,and BS Manjunath,"Modeling Object Classes in Aerial Images Using Texture Motifs," in Proceedings of the IAPR International Conference on Pattern Recognition, 2002,2:981～984.
[66] W. Y. Ma and B. S. Manjunath,"A texture thesaurus for browsing large aerial photographs," Journal of the American Society for Information Science (JASIS), 1998,49(7):633～648.
[67] B. S. Manjunath and W. Y. Ma,"Texture Features for browsing and retrieval of image data," Technical Report CIPR-TR-95-06,July 1995.
[68] D. Andresen,L. Carver,R. Dolin,C. Fischer,J.Frew,M. Goodchild,O.Ibarra,R. Kothuri,M. Larsgaard,B. Manjunath,D. Nebert,J. Simpson,T. Smith,T. Yang,and Q. Zheng. The WWW Prototype of the Alexandria Digital Library. Proceedings of ISDL'95: International Symposium on Digital Libraries,Aug. 1995.
[69] WY Ma and BS Manjunath,"Edge flow: a framework of boundary detection and image segmentation," Proc. IEEE International Conference on Computer Vision and Pattern Recognition(CVPR'97),San Juan,Puerto Rico,June 1997:744～749.
[70] Norbert Strobel,Sanjit K. Mitra,and BS Mmljunath. An Approach to Efficient Storage,Retrieval,and Browsing of Large scale Image Databases. Proceedings of the SPIE, 1995:324～335.
[71] WY Ma and BS Manjunath. A texture thesaurus for browsing large aerial photographs ",Journal of the American Society for Information Science,Wiley for ASIS, 1998,49(7):633～648.
[72] Chung-Sheng Li,Lawrence D. Bergman,Vittorio Castelli,John R. Smith: SPIRE: A Progressive Content-Based Spatial Image Retrieval Engine. SIGMOD Conference 2000: 598(Berkeley Digital Library Project),2000.
[73] Chad Carson,Megan Thomas, Serge Belongie,Joseph M. Hellerstein, and Jitendra Malik,Blobworld: A system for region-based image indexing and retrieval,Third Int. Conf. on Visual Information Systems,June 1999.
[74] http://elib.cs.berkeley.edu/photos/blobworld/.
[75] Julio Barros,James French,Worthy Martin,Patrick Kelly, and James M. White. Indexing multispeetral images for content-based retrieval. In Proceedings of the 23rd AIPR Workshop on Image and Information Systems: Applications and Opportunities,Washington,D.C.,October 1994.
[76] Julio Barros,James. French,Eorthy Martin, " System for Indexing Multi-spectral Satellite Images for Efficient Content-Based Retrieval," in Storage and Retrieval for Image and Video Databases Ⅲ,W. Niblack,R. C. Jain,editors,Proc. SPIE 2420,Feb. 1995:228～237.
[77] A. Vellaikal and C.-C. Jay Kuo,Content-based image retrieval using multiresolution histogram representation,SPIE's Photonics East,Philadelphia, PA, Oct. 1995:22～26.
[78] A. Vellaikal,Xia Wan and C.-C. Jay Kuo,Content-based retrieval of color and multispectral images,International Symposium on Communications,National Taiwan University, Taipei,TAIWAN,ROC,Dec. 1995:27～29.
[79] A.Vellaikal,C. Kuo,and S. Dao: "Content-based retrieval of remote sensed images using vector quantization",Proceedings of SPIE, 1995:178～189.
[80] A.Vellaikal,Son Dao and C.-C.Content-Based Retrieval of Remote Sensed Images Using a Feature-Based Approach,1995.
[81] A.Vellaikal,C.-C. Jay Kuo,Son Dao,Content-Based Retrieval of Color and Multispectral Images Using Joint Spatial-Spectral Indexing, SPIE,1995:232～243.
[82] A.Vellaikal,S. Dao and C.-C. Jay Kuo,Feature-based representation to aid content-based retrieval of remote sensed images,Science Information Management and Data Compression Workshop,Greenbelt,MD. Oct. 1995:26～27.
[83] A.Vellaikal,C.-C. Jay Kuo and S. Dao,Content-based retrieval and browsing of remote sensed images with VQ,SPIE's International Symposium on Aerospace/Defense Sensing and Dual-Use Photonics,Orlando,Florida,April 1995:17～21.
[84] A.Vellaikal and C.-C. Jay Kuo,Joint spatial-spectral indexing for image retrieval,IEEE International Conference on Image Processing,Lausanne,Switzerland, Sept. 1996:16～19.
[85] Manjunath BS and WY Ma,Texture features for browsing and retrieval of image data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996,18(8):837～842.
[86] G. Sheikholeslami,Wang,Wenjie; Zhang,Aidong. A Model of Image Representation and Indexing in Image Database Systems,July 20,1998.
[87] G.Sheikholeslami,A. Zhang and L. Bian," A Multi-Resolution Content-Based
Retrieval System for Geographical Images",GeoInformatica,An International Journal on Advances of Computer Science for Geographic Information Systems,1999,3(2):109-139.
[88] G. Sheikholeslami,A. Zhang,and L. Bian," Geographical Image Classification and Retrieval ",The Proceedings of the 5th ACM Workshop on Geographic Information Systems,Las Vegas,Nevada,November 1997:58～61.
[89] Chung-Sheng Li; Castelli,V.;Deriving texture feature set for content-based retrieval of satellite image database,ICIP(1), 1997:576～579.
[90] Lawrence D. Bergman,Vittorio Castelli,and Chung-Sheng Li,Progressive Content-Based Retrieval from Satellite Image Archives,D-Lib Magazine,October 1997.
[91] V.Castelli,L. D. Bergman,C.-S. Li,J. R. Smith and A.Thomasian. Progressive Content-based Retrieval of Satellite Image Database through Internet. IEEE Tyrrhenian Intern. Workshop on Digital Comm.,Ischia,Italy, Sept. 1998.
[92] H. Rehrauer and K. Seidel and M. Datcu,Multiscale Markov Random Fields for Large ImageDatasets Representation, In: IEEE Intemational Geoscience and Remote Sensing Symposium,IGARSS'97,1997.
[93] H.Rehrauer and K. Seidel and M. Datcu, Multiscale Image Segmentation with a Dynamic Label Tree,In: IEEE International Geoscience and Remote Sensing Symposium,IGARSS'98,1998.
[94] H.Rehrauer, K.Seidel,and M. Datcu,Bayesian Image Segmentation using a Dynamic Pyramidal Structure,Proceedings of the 18th International Workshop on Maximum Entropy and Bayesian Methods (MaxEnt'98) 1998.
[95] H.Rehrauer, K.Seidel and M. Datcu,"Multi-scale Indices for Content-based Image Retrieval",IEEE International Geoscience and Remote Sensing Symposium, IGARSS,99,1999.
[96] Hubert Rehrauer, Klaus Seidel,Mihai Datcu, Characteristic Scale Detection in Remote-sensing Data ,IGARSS'99,1999.
[97] K.Seidel and M. Schr?der and H. Rehrauer and M. Datcu,Meta Features for Remote Sensing Image Content Indexing,In: IEEE International Geoscience and Remote Sensing Symposium,IGARSS'98,1998.
[98] K.Seidel,M.Schr?der, H. Rehrauer,G.Schwarz and M. Datcu,"Query by Image Content from Remote Sensing Archives",IEEE International Geoscience and Remote Sensing Symposium (IGARSS'98), 1998:393～396.
[99] M. Datcu,K. Seidel,M. Schr?der, H. Rehrauer, C. Palubinskas and M. Walessa,"Image Information Mining and Remote Sensing Data Interpretation",IEEE International Geoscience and Remote Sensing Symposium (IGARSS2000),2000.
[100] M.Schr?der and K. Seidel and M. Datcu,Gibbs Random Field Models for Image Content Characterization,In: IEEE International Geoscience and Remote
Sensing Symposium,IGARSS'97,1997.
[101] M. Schr?der and K. Seidel and M. Datcu, User-oriented Content Labeling in Remote Sensing Image Archives,In: IEEE International Geoscience and Remote Sensing Symposium,IGARSS'98,1998.
[102] M. Datcu,K. Seidel,and M. Walessa, Spatial information retrieval from remote sensing images-Part Ⅰ: Information theoretical perspective,In: IEEE Trans. on Geoscience and Remote Sensing, 1998,36(5): 1431～1445.
[103] M. Schr?der, H. Rehrauer, K. Seidel and M. Datcu,Spatial information retrieval from remote sensing images-Part Ⅱ: Gibbs Markov Random Fields,in: IEEE Trans. on Geoscience and Remote Sensing, 1998,36(5): 1446～1455.
[104] M. Schr?der, K. Seidel,and M. Datcu,Bayesian labeling of remote sensing image content,Proceedings of the 18th International Workshop on Maximum Entropy and Bayesian Methods (MaxEnt'98), 1998.
[105] M. Schr?der and A. Dimai,Texture Information in Remote Sensing Images: A Case Study, Workshop on Texture Analysis (WTA'98),Freiburg,1998.
[106] P.Agouris,J. Carswell,A. Stefanidis: An Environment for Content-Based Image Retrieval from Large Spatial Databases,ISPRS Journal of Photogrammetry and Remote Sensing,Elsevier, 1999,54(4):263～272.
[107] P.Agouris.,A. Stefanidis & J. Carswell "Intelligent Retrieval of Digital Images from Large Geospatial Databases",International Archives of Photogrammetry and Remote Sensing,Vol. ⅩⅩⅫ,Part 3/1, 1998:515～522.
[108] P.Agouris and A. Stefanidis.Intelligent Image Retrieval from Large Databases Using Shape and Topology, IEEE International Conference on Image Processing (ICIP) '98,Vol. 2,pp. Ⅱ779-Ⅱ783,Chicago,Oct. 1998.
[109] Qimin CHENG; Chongjun YANG, Zhenfeng SHAO, Feixiang CHEN Application of M-Band Wavelet Theory to Texture Analysis in Content-Based Aerial Image Retrieval, IGARSS'04.
[110] B.Raghunathan and S.T. Acton,Content based retrieval for remotely sensed imagery, Proc. IEEE Southwest Symposium on Image Analysis and Interpretation,Austin,Texas,April,2000.
[111] Luis M. del Val Cura,Neucimar Jer?nimo Leite and Claudia Bauzer Medeiros,"An Architecture for Content-Based Retrieval of Remote Sensing Images".IEEE International Conference on Multimedia and Expo (Ⅰ) ,2000:303～306.
[112] Irwin E. Alber, Ziyou Xiong,Nancy Yeager, Morton Farber and William M. Pottenger. "Fast Retrieval of Multi-and Hyperspectral Images Using Relevance Feedback". Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS 2001). July 2001,3:1149～1151.
[113] Irwin E. Alber, Morton Farber, Nancy Yeager, Ziyou Xiong,and William M. Pottenger. "Retrieval of Multi-and Hyperspectral Images Using an Interactive
Relevance Feedback form of Content-based Image Retrieval". Proceedings of the15th Annual International Symposium on Aerospace/Defense Sensing, Simulation,and Controls,Orlando,FL,April 2001:4384～4388.
[114] Luo Rui,Zhang Yongsheng,Fan Yonghong,Deng Xueqing,Research on content-based remote sensing image retrieval: the strategy for visual feature selection,extraction,description and similarity measurement,Info-tech and Info-net,2001. Proceedings. ICⅡ 2001-Beijing. 2001 International Conferences on, 2001,1:21～325.
[115] O. Kao,T. Bretschneider, GR. Joubert,Image Retrieval with Gabor-Wavelet-Networks,Proceedings of the International Conference on Internet and Multimedia Systems and Applications,2002:312～317.
[116] O. Kao,I. la Tendresse,CLIMS-A system for image retrieval by using colour and wavelet features,Advances in Information Systems,Proceedings of the First Biennial International Conference on Advances in Information Systems (ADVIS'2000),Lecture Notes in Computer Science,LNCS 1909,Springer Verlag, 2000:238～248.
[117] O. Kao,I. la Tendresse,On the Impact of the Number of Coefficients on the Quality of Wavelet-based Image Retrieval,Proceedings of the 2001 International Conference on Imaging Science,Systems,and Technology,2001:361～372.
[118] O. Kao,I. la Tendresse,Dynamic Image Retrieval with Wavelet Coefficients,Proceedings of the 2002 International Conference on Imaging Science,Systems,and Technology, CSREA Press, 2002,2:477～480.
[119] O. Kao,T. Bretschneider, GR. Joubert,Image retrieval with Gabor-Wavelet-Networks,Proceedings of the 6th International Conference on Internet and Multimedia Systems and Applications (IMSA 2002), 2002:312～317.
[120] T. Bretschneider, O. Kao,Retrieval of Multispectral Satellite Imagery on Cluster Architectures,Proceedings of the EuroPar, Lecture Notes in Computer Science, 2002:342～346.
[121] T. Bretschneider, O. Kao,A Retrieval system for remotely sensed imagery, Proceedings of the 2002 International Conference on Imaging Science,Systems,and Technology,2002: 439～445.
[122] T.Bretschneider, R. Cavet and O. Kao,"Retrieval of remotely sensed imagery using spectral information content",Proceedings of the International Geoscience and Remote Sensing Symposium, 2002,4:2253～2256.
[123] 吴均．基于内容的图像检索技术及其在遥感图像中的应用研究[博士论文]．北京：中国科学院遥感应用研究所，2001．
[124] 周焰，李德仁，徐长勇．基于形状的遥感图像检索系统，华中科技大学学报(自然科学版)，2003，31(3)：14～16．
[125] 周焰．遥感图像内容查询若干问题的研究[博士后出站报告]．武汉：武汉大学，2003．
[126] JR Smith and SF Chang,Automated Binary Texture Feature Sets for Image Retrieval,International Conference on Acoustics, Speech,and Signal Processing (ICASSP),1996.
[127] 张继贤，李德仁．影象纹理的多尺度分析，环境遥感．1996，11(1)：1～13．
[128] 陈武凡．小波分析及其在图像处理中的应用．北京：科学出版社，2002．
[129] M.K. Mandal and T. Aboulnasr,"Fast Wavelet Histogram Techniques for Image Indexing", Computer Vision and Image Understanding, July/August, 1999, 75(1/2):99～110.
[130] 程起敏，杨崇俊，邵振峰．基于小波变换的遥感影像渐进式检索，中国图象图形学报，Vol．8(A)，Spec．2003：413～417．
[131] 秦前清，杨宗凯．实用小波分析．西安：西安电子科技大学出版社，1994．
[132] http://sipi.usc.edu/services/database/database.cgi?volume=aerials.
[133] 朱长青．小波分析理论与影像分析．北京：测绘出版社，1998．
[134] 王智均，李德仁，李清泉．多进制小波理论在SPOT和TM影像融合中的应用，武汉大学学报．信息科学版．2000，26(1)：24～28．
[135] 唐向宏，谢书琴，李奇良，等．M 带小波变换在图像压缩中的应用，电路与系统学报．2003，7(1)：108～111．
[136] Chitre,Y and A.P. Dhawan,"M-Band wavelet discrimination of natural textures",Pattern Recognition, 1999,32(5):773～789.
[137] M. Acharyya and M.K. Kundu,"An adaptive approach to unsupervised texture segmentation using M-Band wavelet transform",Signal Processing, 2001,81:1337～1356.
[138] M. Kokare,B. N. Chatterji and P. K. Biswas ,"M-Band Wavelet Based Texture Features For Content Based Image Retrievar",Electronics and Electrical Communication Engineering Department,ICVGIP2002,Dec. 2002.
[139] 程起敏，杨崇俊，邵振峰．基于多进制小波变换的渐进式纹理图像检索．武汉大学学报．信息科学版(已录用)．
[140] P.W. Huang,S. K. Dai. "Design of A Two-Stage Content-Based Image Retrieval System Using Texture Similarity". Information Processing & Management,2004,40(1):81～96.
[141] E.Remias and G. Sheikholeslami and A. Zhang," Block-Oriented Image Decomposition and Retrieval in Image Database Systems," in the 1996 International Workshop on Multi-media Database Management Systems,Blue Mountain Lake,New York,August, 1996.
[142] 章毓晋．基于内容的视觉信息检索．北京：科学出版社，2003．
[143] 李爽，丁圣彦，等．遥感影像分类方法比较研究，河南大学学报．自然科学版．2002，32(2)：70～73．
[144] 杨翔英，章毓晋．小波轮廓描述及在图像查询中的应用，计算机学报，1999，22(7)：752～757．
[145] K. M?ller, J.R. Ohm: "Wavelet-based contour descriptor," MPEG-7 proposal no. P567,Jan. 1999.
[146] 袁野，欧宗瑛．基于小波变换和模糊算法医学图像边缘检测算法．大连理工大学学报，2002．42(4)．
[147] 姚玉荣，章毓晋．利用小波和矩进行基于形状的图象检索．中国图象图形学报，2000．5(3)．
[148] 薛鸿民，刘志镜，刘利等．基于形状的图像检索的关键技术研究．计算机应用研究2002，19(11)：61～64．
[149] 刘继敏，史忠植．一种基于形状的图像信息检索方法，软件学报．2000，11(1)：110～115．
[150] 袁华，董守斌，张凌，等．GFO和不变矩实现基于形状的图像检索新方法，华南理工大学学报(自然科学版)，2002，30(4)．
[151] 黎夏．形状信息的提取与计算机自动分类．环境遥感，1995，10(4)．
[152] 杨述斌．图象边缘检测技术概述．武汉化工学院学报．2003，25(1)：73～76．
[153] Hu M K. Visual Pattern Recognition by Moment Invariants,IRE Trans. Inform. Theory, 1962,IT-8:179～187.
[154] Chen C C. Improved moment invariants for shape discrimination.Pattern Recognition, 1993,26(5):683～686.
[155] 王波涛，孙景鳌，等．相对矩及在几何形状识别中的应用，中国图象图形学报．A辑．2001，6(3)：296～300．
[156] 边肇祺，模式识别，北京：清华大学出版社，1998．
[157] Mallat S,Hwang W L.,Singularity detection and processing with wavelets,IEEE IT,3,1992,8(2):617～643.
[158] M. Do,So Ayer and M. Vetterli,"Invariant image retrieval using wavelet maxima moment",Proc. Visual '99,Amsterdam,1999.
[159] M.Trimeche,F. Alaya Cheikh and M.Gabbouj,"Similarity Retrieval of Occluded Shapes Using Wavelet-Based Shape Feature",submitted to the SPIE International Symposium on Internet Multimedia Management Systems (VV10),Boston,Massachusetts,USA,November 5-8,2000.
[160] F. Alaya Cheikh,A. Quddus and Moncef Gabbouj,"Multi-level Shape Recognition based on Wavelet-Transform Modulus Maxima," Proc. of Southwest Symposium on Image Analysis and Interpretation,SSIAI 2000,Austin,Texas,USA,April 2-4,2000:8～12.
[161] F.Alaya Cheikh,A.Quddus and M.Gabbouj,"Contour-based Object Recognition using Wavelet-Transform",European Signal Processing Conference,Eusipco-2000,Tampere,Finland,September 5-8,2000.
[162] F. Alaya Cheikh,A. Quddus and M. Gabbouj,"Shape Recognition based on Wavelet-Transform Modulus Maxima", 7th IEEE International Conference on Electronics,Circuits and Systems (ICECS2K),pp.
461-464,Beirut,Lebanon,December 17-19,2000.
[163] A.Quddus,EAlaya Cheikh and M.Gabbouj,"Wavelet-based Multi-level Object Retrieval In Contour Images",Proc. of the International Workshop on Very Low Bit Rate Video Coding (VLBV'99),Kyoto,Japan,October 29-30,1999.
[164] 李德仁，陈晓勇．用数学形态学变换自动生成DTM三角形格网的方法测绘学报．1990,19(3)：161～172．
[165] 戴青云，余英林．数学形态学在图象处理中的应用进展．控制理论与应用．2001，18(4)：478～482．
[166] 王宇，王乘，刘吉平．一种基于数学形态学的遥感图像边缘检测算法，重庆邮电学院学报．自然科学版．2003，15(2)：57～60．
[167] BibRef. Lee, JS,Haralick, RM, Shapiro, LG, Morphologic Edge Detection, J-RA(3), 1987.
[168] 杨述斌，彭复员，等．基于形态学的遥感图像全方位边缘检测算法研究．遥感信息．2003，1：2～3，47．
[169] 朱晓亮，彭复员，等．基于多尺度形态学的弱目标图像处理方法．红外与激光工程．2002．31(6)：482～484．
[170] M. K. Kundu,B. Chanda and Y. V. Padmaja,"Morphologic edge detection with multi scale approach",in Proc.IEEE Int. Conf. TENCON'98, New Delhi,India, 1998:53～56.
[171] Bhabatosh Chanda, Malay K. Kundu And Y. Vani Padmaja, A MULTI-SCALE MORPHOLOGIC EDGE DETECTOR,Pattern Recognition, October, 1998,31(10):1469～1478.
[172] 崔新春，裘季冰，等．基于小波变换和数学形态学的人造景物提取．上海师范大学学报．自然科学版．2002，31(1)：42～46．
[173] 陶洪久，柳健，等．基于小波变换和数学形态学的遥感图像边缘检测．红外与激光工程．2002，31(2)：154～157．
[174] 张艳宁，郑江滨，王晓红，等．一种有效的遥感图像目标识别方法．信号处理，2002，18(1)．
[175] 刘循，游志胜，多尺度形态学图像边缘检测方法光电工程．2003，30(3)：56～58
[176] 周维忠，孙国基，基于多尺度数学形态学的边缘检测数据．采集与处理．2000，15(3)：316～319．
[177] 吕铁英，彭嘉雄．一种基于数学形态学的图象多尺度分析方法的研究．数据采集与处理．1998，13(2)：107～111．
[178] 冯玉才，曹奎，等．一种支持快速相似检索的多维索引结构．软件学报．2002，13(8)：1678～1685．
[179] 刘芳洁，董道国，薛向阳．度量空间中高维索引结构回顾．计算机科学．2003，30(7)：64～68．
[180] 董道国，薛向阳，罗航哉．多维数据索引结构回顾．计算机科学，2002，29(3)：1～6
[181] 梅承力，周源华．高维数据空间索引的研究．红外与激光工
程．2002，31(1)：77～81．
[182] 刘传才．基于可视特征度量距离的图像检索．福州大学学报(自然科学版)．2001，29(2)：12～15．
[183] Bozkaya T. Ozsoyoglu M. Distance-based indexing for high-dimensional metric spaces. ACM SIGMOD Intl. Conf. On Management of Data,Sigmod Record, 1997,26(2):357～368.
[184] A.Guttman. R-trees: a dynamic index structure for spatial searching. In ACM SIGMOD International Conference on Management of Data,pages 47～57,Boston,MA,June 1984.
[185] T. Sellis,N. Roussopoulos,and C. Faloutsos,"The R + -tree: A dynamic index for multidimensional objects," in Proceedings of Very Large Data Bases, Brighton,England, 1987:3～11.
[186] N. Beckmann,H.-P. Kriegel,R. Schneider, and B. Seeger, The R*-tree: An efficient and robust access method for points and rectangles,Proceedings of ACM SIGMOD Int'l. Conf. on Management of Data, 1990:322～331.
[187] D. A. White and R. Jain. Similarity indexing with the SS-tree. In International Conference on Data Engineering (ICDE),New Orleans,LA,Mareh 1996:516～523.
[188] Berchtold S.,Keim DA,Kriegel HP, The X-Tree: An Index Structure for High-Dimensional Data,Proc. 22th Int. Conf. on Very Large Data Bases,Bombay, India, 1996.
[189] N. Katayama and S. Satoh,The sr-tree: An index structure for high-dimensional nearest neighbor queries,Proceedings of ACM SIGMOD,May 1997.
[190] Nievergelt,J.,H. Hinterberger, and K. Sevcik,The grid file: An adaptable,symmetric multikey file structure,In A. Duijvestijn and p. Lockemann (Eds.),Proc. 3 rd ECI Conf.,Number 123 in LNCS,Berlin/Heidelberg/New York, Springer-Verlag, 1981:236～251.
[191] Markku Tamminen: The Extendible Cell Method for Closest Point Problems. BIT 22(1), 1982:27～41.
[192] K. Hinrichs: Implementation of the Grid File: Design,Concepts and Experience,BIT 25,1985:569～592.
[193] A. Hutflesz,HW Six,P. Widmayer: The twin grid file: a nearly space optimal index structure. Proc. Int. Conf. on Extending Database Technology,1988.
[194] J. L.Bentley, Multidimensional Binary Search Trees Used for Associative Searching,Communications of the ACM 19, 1979.
[195] JT Robinson,The KDB-Tree: A Search Structure for Large Multidimensional Dynamic Indexes,Proc. SIGMOD ACM, 1981:10～18.
[196] Raphael A. Finkel and Jon Louis Bentley. Quad trees: A data structure for retrieval on composite keys. Acta Informatica4,1974:1～9.
[197] H. Samet, 1984,The Quadtree and Related Hierarchical Data
Structures,Computing Surveys 16,187.
[198] Roger Weber, Hans-J?rg Schek,Stephen Blott,A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces,Proceedings of the 24rd International Conference on Very Large Data Bases, 1998:194～205.
[199] Y.Sakurai,M.Yoshikawa,S.Uemura and H. Kojima, The A-tree: An Index Structure for High-Dimensional Spaces Using Relative Approximation,Proc. 26th Int. Conf. on Very Large Data Bases (VLDB2000), 2000:516～526.
[200] C. Faloutsos and K.-I.(David) Lin,Fastmap: A fast alogorithm for indexing,data-mining and visualization of traditional and multimedia datasets,in Proc. of SIGMOD,1995:163～174.
[201] R. Ng and A. Sedighian,Evaluating multi-dimensional indexing structures for images transformed by principal component analysis,in Proc. SPIE Storage and Retrieval for Image and Video Databases,1996.
[202] S.Chandrasekaran,B. S. Manjunath,Y. F. Wang,J. Winkeler, and H. Zhang,An eigenspace update algorithm for image analysis,CVGIP: Graphical Models and Image Processing Journal,1997.
[203] Burkhard,W.A.; Keller, R.M.; Some approaches to Best-Match File Searching, Comm. ACM.,16,4. 1973.
[204] Baeza-Yates R, Cunto W, Manber U, Wu, S, Proximity matching using fixed-queries trees, In: Proc. 5th Combinatorial Pattern Matching (CPM'94), LNCS 807,1994:198～212.
[205] Baeza-Yates R. Searching: an algorithmic tour. Encyclopedia of computer science and technology, Marcel Dekker Inc. 1997,37:331～359.
[206] Chavez E,Marroquin J,Navarro G. Overcoming the curse of dimensionality. European Workshop on Content-based multimedia Indexing (CBMI'99),1999:57～64.
[207] Uhlmann J. Satisfying general proximity/similarity queries with metric trees. Information Processing Letters, 1991,40:175～179.
[208] Yianlios Y. Locally lifting the curse of dimensionality for nearest neighbor search. DEMACS Implementation Challenge,ALENEX'99,Baltimore,MD,1999.
[209] Kalantari I,McDonald G. A data structure and an algorithm for the nearest point problem. IEEE Transactions on Software Engineering, 1983.
[210] Brin S. Near neighbor search in large metric spaces. Proc. 21st Conf. On Very Large Database (VLDB'95), 1995:574～584.
[211] Dehne F, Nolteimer H, Voronoi trees and clustering problems, Information Systems, 1987,12(2): 171～175.
[212] P.Ciaccia,M. Patella,P. Zezula; M-Tree: An Efficient Access Method for Similarity Search in Metric Spaces; Proc. of the 23rd VLDB Conference, 1997:426～435.
[213] G. Navarro; Searching in metric spaces by spatial approximation; In: Proc. String Processing and Information Retrieval(SPIRE'99),IEEE CS Press,1999:141～148.
[214] E V Ruiz,An algorithm for finding nearest neighbours in (approximately) constant average time, Pattern Recognition Letters, 1986,4(3): 145～157.
[215] Maria Luisa Mic?,Jos? Oncina, Enrique Vidal,A new version of the nearest-neighbour approximating and eliminating search algorithm (AESA) with linear preprocessing time and memory requirements,Pattern Recognition Letters, 1994,5(1):9～17.
[216] T. Bozkaya and M. Ozsoyoglu. Indexing large metric spaces for similarity search queries. ACM Transaction on Database Systems, 1999,24(3):361～404.
[217] Ada Wai-Chee Fu,Polly Mei-shuen Chan,Yin-Ling Cheung, and Yiu Sang Moon. Dynamic vp-tree indexing for n-nearest neighbor search given pair-wise distances. VLDB Joumal, 2000,9(2): 154～173.
[218] Xiangmin Zhou,Guoren Wang,Jeffrey Xu Yu,Ge Yu: M+-tree: A New Dynamical Multidimensional Index for Metric Spaces. ADC 2003:161～168.
[219] Rui Mao,Wenguo Liu,et. On index methods for an image database. MMDB'03. (submitted).
[220] Neha Singh,Analysis of search algorithms and tree structures for proximity search in metric spaces,Undergraduate Honors Thesis,Department of Computer Sciences,The University of Texas at Austin,Fall 2002.
[221] Edgar Ch?vez,Gonzalo Navarro, Ricardo Baeza-Yates, Jos? Luis Marroqu?n, Searching in metric spaces, ACM Computing Surveys (CSUR), 2001,33(3):273～321.
[222] 王小平，曹立明．遗传算法——理论、应用与软件实现．西安：西安交通大学出版社．2002．
[223] 陈国良，王煦法，庄镇泉，等．遗传算法及其应用．重庆：人民邮电出版社．2001．
[224] 张笃振，李一民．基于遗传算法的彩色图象分割．昆明理工大学学报(理工版)，2003，28(4)：57～59．
[225] 施建强，刘晓平．基于遗传算法的数据挖掘技术的研究．电脑与信息技术，2003．1：9～15．
[226] 刘勇国，李学明，张伟，等．基于遗传算法的特征子集选择．计算机工程，2003，29(6)：19～21．
[227] 吴佳英，郑金华，遗传算法的研究与发展动向．衡阳师范学院学报(自然科学)，2003，3(3)：30～33．
[228] 徐丽娜，邓正隆，遗传算法与最优化，黑龙江自动化技术与应用，1996，15(4)：1～4．
[229] 邵红，崔文成，张健武等，遗传算法在基于内容的图像检索中的应用．计算机工程，2003．29(16)：21～22．
[230] 翟宜峰，李鸿雁，刘寒冰等，用遗传算法优化神经网络初始权重的方法．吉林大学学报(工学版)，2003，33(2)：45～50．
[231] 李军，费川云．地球空间数据集成研究概况．地理科学进展，2000．
[232] 方艳梅，杨灿，等．支持基于内容检索的图像数据模型的设计．计算机应用．2001，21(7)：23～26．
[233] 葛咏，郭大海．基于Clinet/Server的影像数据库管理模式．计算机工程与应用．2000，36(5)：129～130，189。
[234] 王密，龚健雅．基于扩展关系数据库的遥感影像数据库管理系统的研究与实现．测绘信息与工程．2002，27(5)：1～3．
[235] 罗睿，张永和，等．遥感图像数据库基于内容查询的研究．遥感学报，2002，6(1)：24～29．
[236] 罗睿，张永生，李颖，等．遥感影像数据模型及影像数据库的建立．测绘学院学报，2000，17(4)：273～276．
[237] 周学海，李光亚．按内容检索的图象数据库系统数据模型．软件学报．1998，9(3)：186～189．
[238] 陈跃峰，肖自美．基于内容查询的图象数据库系统模型．中国图象图形学报．A辑．1997，2(8)：634～637．
[239] 王密．大型无缝影像数据库系统(GeoImageDB)的研制与可量测虚拟现实(MVR)的可行性研究[博士论文]．武汉：武汉测绘科技大学，2001．
[240] 虞万荣，张银福，等．图像数据库Web检索接口的设计与实现，微型电脑应用．2002，18(3)：24～25，9．
[241] 万志坚，李文锋．Web环境下基于内容查询的图像数据库研究．计算机工程与应用．2002，38(11)：207～208，215．
[242] 李文锋，万志坚．基于Web的图像数据库系统的设计与实现．计算机应用研究．2002，19(5)：82～83，94．
[243] 葛艳红，李文锋，万志坚．利用ISAPI,Java开发B/S模式的图像数据库．武汉理工大学学报．交通科学与工程版．2003，27(3)：388～390．
[244] 章毓晋．WWW上基于内容的图象检索系统．电子技术应用．1999，25(12)：11～13．
[245] Hongjiang Zhang, Liu Wenyin,Chunhui Hu,"iFind—A System for Semantics and Feature Based Image Retrieval over Internet",ACM MULTIMEDIA 2000--The 8th ACM International Multimedia Conference,Los Angeles,California,October 30-November 3,2000.
[246] G.B. Marchisio,J. Cornelison: "Content-based search and clustering of remotely sensing imagery",Proceedings of the IEEE International Conference on Geoscience and Remote Sensing, 1999,1:290～292.
[247] G.B. Marchisio,J. Cornelison: "Content-based search and clustering of remotely sensing imagery",Proceedings of the IEEE International Conference on Geoscience and Remote Sensing, 1999,1:290～292.
[248] GB. Marchisio,WH Li,M. Sannella, and JR Goldschneider,"GeoBrowser:An integrated environment for satellite image retrieval and mining",Proceedings of the IEEE International Geoscience and Remote Sensing Symposium,1998,2:669～673.
[249] Chung-Sheng Li,J. J. Turek,and E. Feig. Progressive template matching for content-based retrieval in earch observing satellite image databases. Proc. of SPIE Photonics East,November 1995.
[250] Chung-Sheng Li,John Turek: Content-Based Indexing of Earth-Observing Satellite Image Database with Fuzzy Attributes. Storage and Retrieval for Image and Video Databases(SPIE),1996:438～449.
[251] D.G Healey, A. Jain. Retrieving multispectral satellite images using physics-based invariant representations, IEEE Transactions on Pattem Analysis and Machine Intelligence, 1996,18(8):842～848.
[252] T. Smith. A digital library for geographically referenced materials. IEEE Computer,29(5): 54～60,1996.
[253] Marshall C. Ramsey ,Hsinchun Chen,Bin Zhu,Bruce R. Schatz,A collection of visual thesauri for browsing large collections of geographic images,Joumal of the American Society for Information Science,1999,50(9):826～834.
[254] Wu,P.,Manjunath,BS,Newsam,SD, Shin,HD,A texture descriptor for browsing and similarity retrieval,SP:IC(16),2000,1-2:33～43.
[255] Zhu,B.,Ramsey, M.,Chen,H.,Creating a Large-Scale Content-Based Airphoto Image Digital Library, IP(9),2000,1:163～163.
[256] Smeulders A,Worring M,Santini S,Gupta A, and Jain R, Content based image retrieval at the end of the early years, IEEE Trans PAMI, 2000,22(12):1349～1380.
[257] 陈剑赟，老松扬，吴玲达．基于内容的图像检索的发展最新趋势．计算机工程与应用，2002，138(10)：47～49．
[258] 胡著智，王慧麟，陈钦峦．遥感技术与地学应用．南京：南京大学出版社，1999．
[259] 贾永红．计算机图像处理与分析．武汉：武汉大学出版社，2001．
[260] 程起敏，杨崇俊，邵振峰．基于内容的图像检索技术研究及前景．武汉大学学报．信息科学版(增刊)．2003．3．
[261] 王文惠．基于内容的图像检索技术的研究和发展．计算机工程与应用，2001．
[262] 张永生．遥感图象信息系统．北京：科学出版社，2000．
[263] 朱述龙，张占睦．遥感图象获取与分析．北京：科学出版社，2000．