基于内容的图像检索技术研究与实现
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摘要
基于内容的图像检索CBIR(Content-based Image Retrieval)是多媒体信息处理的研究热点之一,有着广泛的应用背景。本文主要针对现有基于内容检索方法对特定的应用有较强的依赖性,特定应用背景中采用不同的特征提取方法对检索效果有较大影响的问题,分别从颜色特征、纹理特征和形状特征三方面详细分析了现有的特征提取方法,并提出了一种改进的形状特征提取方法。另外本文对被广泛关注的基于语义图像检索方法和基于压缩域的图像检索方法进行了相应的研究。在此基础上,构建了一个实用的图像检索系统原型。本文的主要工作有:
在图像特征提取方面,分别从颜色特征、纹理特征和形状特征三个方面进行了分析和研究,并实现了颜色直方图、分块颜色直方图、累计颜色直方图、颜色矩、共生矩阵、Gabor滤波器组、高斯马尔科夫随机场、形状参数、Hu矩和Zernike矩等常见的特征提取方法。在形状特征的提取上,本文提出了一种“基于遗传算法的自适应边界点选取形状上下文”的特征提取方法。实验表明,与传统的形状上下文方法相比该方法具有较好的检索性能。
在语义检索方面,在分析常用的语义检索方法的基础上,本文提出了一种基于区域的图像检索方法,实验证明该方法具有较好的检索效果。另外在相关反馈方面,本文实现了一种加权查询向量修改反馈方法,通过实验证明了该方法的有效性。
在基于压缩域的图像检索方面,对现有的基于压缩域的图像检索技术进行了分析,并介绍了基于JPEG和JPEG2000压缩标准的检索方法,最后实现了一种基于JPEG图像的压缩域纹理图像检索方法,实验证明该方法具有较好的检索性能。
基于以上研究,设计并实现了一个高效、灵活的图像检索原型系统。该原型系统可以方便的利用本文中介绍的各种检索算法进行图像检索,并可以根据用户对检索结果主观评价的程度进行相关反馈。另外,在用户接口上,系统提供直接选择、随机选择和浏览选择三种查询图像选择方法,使用户可以以更加人性化的方式选择查询图像。实验证明,本文实现的系统具有较好的检索性能。
Content-based image retrieval (CBIR) is a hotspot of research in multimedia information processing field, and has capacious developed foreground. CBIR seems to be highly task-dependent. In practice, special algorithms are taken according to special application. To slove this problem, this paper analyzed the method of feature extraction, and presented a new method of shape feature extraction. In addition, we study the technology of semantic retrieval and compressed domain image retrieval. On the basis of it, a system prototype of CBIR is designed.On the aspect of feature extraction, we analyzed separately color feature extraction, texture feature extraction and shape feature extraction and realized frequently used methods, like color histogram, co-occurrence matrix and so on. On the basis of shape context, a new shape feature extraction method called Shape context by choosing the edge points self-adaptively based on genetic algorithm is proposed. Experiments show that the method acquired better effect compared with shape context.On the aspect of semantic retrieval, we proposed a new method based on object region on the basis of analyzing frequently used semantic retrieval methods. In addition, to overcome the drawback of query vector modified feedback method, a new method named weighted query vector modified is realized. Experiments show the methods are satisfactory.On the aspect of compressed domain image retrieval, we analyzed the retrieval methods based on JPEG and JPEG2000 on the basis of analyzing frequently used compressed domain image retrieval methods. At last, we realized a method based on JPEG image, Experments show the method aquired better retrieval effect.A system prototype of content-based image retrieval is designed and realized on the basis of it. The system can retrieval image using the methods of this paper, and user can feedback by his subjective evaluation. In the user interface, we designed three query image select method. By this way, user can select query image easily. Experiments show that the system acquired better retrieval effect.
在图像特征提取方面,分别从颜色特征、纹理特征和形状特征三个方面进行了分析和研究,并实现了颜色直方图、分块颜色直方图、累计颜色直方图、颜色矩、共生矩阵、Gabor滤波器组、高斯马尔科夫随机场、形状参数、Hu矩和Zernike矩等常见的特征提取方法。在形状特征的提取上,本文提出了一种“基于遗传算法的自适应边界点选取形状上下文”的特征提取方法。实验表明,与传统的形状上下文方法相比该方法具有较好的检索性能。
在语义检索方面,在分析常用的语义检索方法的基础上,本文提出了一种基于区域的图像检索方法,实验证明该方法具有较好的检索效果。另外在相关反馈方面,本文实现了一种加权查询向量修改反馈方法,通过实验证明了该方法的有效性。
在基于压缩域的图像检索方面,对现有的基于压缩域的图像检索技术进行了分析,并介绍了基于JPEG和JPEG2000压缩标准的检索方法,最后实现了一种基于JPEG图像的压缩域纹理图像检索方法,实验证明该方法具有较好的检索性能。
基于以上研究,设计并实现了一个高效、灵活的图像检索原型系统。该原型系统可以方便的利用本文中介绍的各种检索算法进行图像检索,并可以根据用户对检索结果主观评价的程度进行相关反馈。另外,在用户接口上,系统提供直接选择、随机选择和浏览选择三种查询图像选择方法,使用户可以以更加人性化的方式选择查询图像。实验证明,本文实现的系统具有较好的检索性能。
Content-based image retrieval (CBIR) is a hotspot of research in multimedia information processing field, and has capacious developed foreground. CBIR seems to be highly task-dependent. In practice, special algorithms are taken according to special application. To slove this problem, this paper analyzed the method of feature extraction, and presented a new method of shape feature extraction. In addition, we study the technology of semantic retrieval and compressed domain image retrieval. On the basis of it, a system prototype of CBIR is designed.On the aspect of feature extraction, we analyzed separately color feature extraction, texture feature extraction and shape feature extraction and realized frequently used methods, like color histogram, co-occurrence matrix and so on. On the basis of shape context, a new shape feature extraction method called Shape context by choosing the edge points self-adaptively based on genetic algorithm is proposed. Experiments show that the method acquired better effect compared with shape context.On the aspect of semantic retrieval, we proposed a new method based on object region on the basis of analyzing frequently used semantic retrieval methods. In addition, to overcome the drawback of query vector modified feedback method, a new method named weighted query vector modified is realized. Experiments show the methods are satisfactory.On the aspect of compressed domain image retrieval, we analyzed the retrieval methods based on JPEG and JPEG2000 on the basis of analyzing frequently used compressed domain image retrieval methods. At last, we realized a method based on JPEG image, Experments show the method aquired better retrieval effect.A system prototype of content-based image retrieval is designed and realized on the basis of it. The system can retrieval image using the methods of this paper, and user can feedback by his subjective evaluation. In the user interface, we designed three query image select method. By this way, user can select query image easily. Experiments show that the system acquired better retrieval effect.
引文
[1] W Y Ma, Yining Deng, Tools for texture/color based search of images, Proc. SPIE: Human Vision and Electronic Imaging Ⅱ, 1997
[2] M Stricker, M Orengo, Similarity of color images, Proc. SPIE: Storage and Retrieval for Image and Video Databases Ⅲ, 1995
[3] John R Smith, Shih-Fu Chang, Tools and techniques for color image retrieval, Proc. SPIE: Storage and Retrieval for Image and Video Database, 1995
[4] G Pass, R Zabih, Histogram refinement for content based image retrieval, IEEE Workshop on Applications of Computer Vision, 1996, pp. 96-102
[5] M Tuceryan, A K. lain, Texture analysis, Handbook of Pattern Recognition and Computer Vision, World Scientific;1993
[6] H Tamura, S Mod, T Yamawaki, Textural features corresponding to visual perception, IEEE Trans On Systems, Man and Cybermetics, 1978, Vol. Smc-8, No. 6
[7] Robert M Haralick, K Shanmugam, Textural features for image classification, IEEE Trans. On Sys, Man, and Cyb, 1973, SMC-3(6): 610-621
[8] H Zhang, D Zhong, A scheme for visual feature based image indexing, Proc. SPIE: Storage and Retrieval for Image and Video Databases Ⅲ, 1995
[9] T Chang, C C Jay Kuo, Texture analysis and classification with tree-structured wavelet transform, IEEE Trans. On Image Processing, 1993, Vol. 2, No. 4, pp. 429-441
[10] 徐曼,刘炜,韦志辉,基于内容的图像检索技术,计算机应用,2001,Vol.21,No.9
[11] 王涛,刘文印,孙家广等,傅立叶描述子识别物体的形状,计算机研究与发展,2002,Vol.39,No.12
[12] Yue ting Zhuang, Intelligent multimedia information analysis and retrieval with application to visual design, Ph. D thesis, Zhejiang University, 1998
[13] S Belognie, Shape Matching and Object Recognition Using Shape Contexts, IEEE Trans. Pattern Analysis and Machine Intelligence, 2002, Vol. 24, No. 24
[14] M K Hu, Visual pattern recognition by moment invariants, Computer methods in Image analysis, IEEE computer Society, 1977
[15] S K Chang, 0 Y Shi, C Y Yah, Iconic indexing by 2-D strings, IEEE Trans. Pattern Anal. Machine Intelligence, pp 413-428, 1987
[16] V N Gudivada, V V Raghavan, Design and evaluation of algorithms for image retrieval by spatial similarity, ACM Trans on Information Systems, 1995, Vol. 13, No. 2
[17] 梅承力,基于内容的图像检索关键技术研宄,上海交通大学博士论文,2001
[18] D White, R Jain, Similarity indexing: Algorithms and performance, Proc. SPIE: Storage and Retrieval for Image and Video Database, 1996
[19] G Salton, M J McGill, Introduction to Modem Information Retrieval, McGraw-Hill Book Company, New York, 1982
[20] 朱旭娟,一种基于压缩域图像检索系统开发,北京工业大学学位论文,2004
[21] Moses Charikar, Chandra Chekur, Incremental Clustering and Dynamic Information Retrieval, Proc of the 29th Annual ACM Symposium on Theory of Computing, 1997
[22] 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,2002.10
[23] Maria-Luiza, Anionic, Application of Data Mining Techniques for Medical Image Classification, 2nd International Workshop on Multimedia Data Mining, 2001
[24] AVailaya, H J Zhang, On Image Classification: City vs Landscape, IEEE CBAIVL, 1998
[25] W M Arnold, Marcel Worring, Content-Based Image Retrieval at the End of the Early Years, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, Vol. 22, No. 12
[26] 黄祥林,图像检索中的关键技术,测控技术,2002,Vol.21,No.5
[27] 黄祥林,沈兰荪,基于内容的图像检索技术研究,电子学报,2002,V01.30,No.7
[28] 胡军,耿国华,周明全,一个图像检索模型及其应用,西北大学学报(自然科学版),1999,Vol.29,No.6
[10] 徐曼,刘炜,韦志辉,基于内容的图像检索技术,计算机应用,V01.21,No.9,2001
[29] Myron Flickner, Harpreet Sawhney, Query by Image and Video Content: The QBIC System, IEEE, 1995
[30] John R Smith, Shih-Fu Chang, Visually Searching the Web for the Content, Columbia University, 1997
[31] A Pentland, R W Picard, S Sclaroff, Photobook: Content-Based Manipulation of Image Databases, Proc. SPIE: Storage and Retrieval Image and Video Database Ⅱ, 1994
[32] 黄元元,郭丽,杨静宇,基于主色调匹配的图像检索方法,计算机工程,2002,Vol.28,No.6
[33] 阴炳皓,赵臣,韩晓军,基丁改进的HSI空间模型的目标搜索方法,河北工业大学学报,2003,Vol.32,No.1
[34] Won Soon Kim, Rae Hong Park, Color Image Palette Construction based on the HSI ColorSystem for Minimizing the Reconstruction Error, IEEE Proceedings of International Conference on Image Processing, 1996
[35] 张全海,施鹏飞,基于HSV空间彩色图像的边缘提取方法,计算机仿真,2000,Vol.17,No.6
[36] 黎弘,翟战强,唐树才,利用ARC/INFO制作彩色晕渲图,计算机工程,1999,Vol.25,No.8
[37] 章毓晋,基于内容的视觉信息检索,北京:科学出版社,2003
[38] 龚声蓉,基于内容的图像检索方法研究,北京航空航天大学博士学位论文,2001
[39] 许金普,基丁内容的图像检索技术研究及其系统实现,天津师范大学研究生学位论文,2005
[40] Hemamalini Raman, Retrieval of Images In Digital Image Libraries, Department of Computer Science Curtin University of Technology
[41] A D Alexandrov, W Y Ma, Adaptive Filtering and Indexing for Image Databases, SPIE: Storage and Retrieval for Image and Video Databases HI, Vol. 2420
[42] Zhou Shaohua, Unsupervised Texture Segmentation Via Gaussian Markov Random Field(GMRF) Model and Filter Bank Decomposition, A project report for partial requirement of ENEE739J, Image Understanding, 2001
[43] 盛文,徐晨曦,杨江平,纹理分析窗大小的高斯.马尔可夫随机场模型估计方法,红外与激光工程,2000,Vol.29,No.6
[44] Smith J R, Chang S E Automated binary texture feature sets for image retrieval. Proceedings of IICAA, 1996, p2239-2242
[45] Mandal M K, Aboulnasr T, Fast wavelet histogram techniques for image indexing. Joumal of Computer Vision and Image Understanding,1999, 75(1),p99-110
[46] Lee Moon-Chuen. Texture classification using dominant wavelet packet energy features. Proceedings of ISSIA,2002, p301-304
[47] Mandal M K. Wavelet based coding and indexing of images and video[ph Ddissertation] , University of Ottawa, Canada,1998
[48] 吕娜,孙扬民,黄国丰,对图像检索应用概况的研究,情报科学,Vol.20,No.3,2002
[49] 赵汗青,沈佐锐,于新文,数学形态学在昆虫分类学上的应用研究—在目级阶元上的应用研究,昆虫学报.2003
[50] 商立群,基于zemike矩的图像识别,西安科技学院学报,2000,20(2):151～153
[51] 秦磊,金丰华,舒华忠,Zernike矩在基于内容的医学图像检索中的应用,计算机与现代化,2003,4:61～63
[52] Y.S. Kim, W.Y. Kim, Content-based trademark retrieval system using a visually salient feature, Image and Vision Computing 16(1998) 931-939
[53] Serge Belongie, Jitendra Malik, Jan Puzicha, Shape Matching and Objects Recognition Using Shape Contexts, IEEE Trans on Pattern Analysis and Machine Intelligence, 2002, Vol. 24, No. 24
[54] 王晓红、杨玲,基于颜色/形状直方图的图像检索方法,情报理论与实践 2003,4
[55] 王惠锋,孙正兴,王箭,语义图像检索研究进展,计算机研究与发展,2002,39(5):513～523
[56] Monika Gorkani and Rosalind W. Picard, Texture orientation for sorting photos at aglance, In Proc. lnt Conf. Pat. Rec,Volumel:459～464, Jerusalem,Israel,Oct, 1994.
[57] Szummer, M.;Picard, R.W. Content-Based Access of Image and Video Database, 1998. Proceedings., 1998 IEEE International Workshop on 3 Jan. 1998 Page(s):42-51
[58] A Vailaya, M Figueiredo, A K Jain etal. Image classification for content2based indexing. IEEE Trans on Image Processing, 2001, 10 (1) : 117～130
[59] 袁磊,曹奎,冯玉才,吴永英,一种基于LSI的图像语义检索技术,华中科技大学学报(自然科学版),2002,30(2):105～107
[60] Y Wu, Y T Zhuang, Y H Pan. Image retrieval system for Web: Webscope-CBIR. In: IEEE Proc of the llth Int'l Workshop on Database and Expert System s Applications. Greenwich, London, UK: IEEE CS Press 2000.620～624
[61] S Sclaroff, M L Cascia, L Taycher etal. Unifying textual and visual cues for content-based image retrieval on the World Wide Web. Computer Vision and Image Understanding, 1999,75(1):86～98
[62] 罗法 章毓晋 高永英,基于分析的图像有意义区域提取,计算机学报,2000,vol.23,No.12,1313-1319
[63] 吴冬升 吴乐南 黄波,基于小波模糊聚类区域分割的图像检索,信号处理,2002,vol.18,No.5,,422-426
[64] ByoungChul KO, Hae-Sung Lee, Hyeran Byun, Region-based Image Retrieval System Using Efficient Feature Description, Pattern Recognition, 2000. Proceedings. 15th International Conference on Barcelona, Spain, 2000, Volume 4, 283-286
[65] Mado A. Nascimento, Veena Sridhar, Xiaobo Li, Effective and efcient region-based image retrieval, Journal of Visual Languages and Computing, 2003, No.14, 151-179
[66] 王圆圆 丁志杰 万华林,基于视觉颜色聚类的彩色图像分割,北京理工大学学报,2003,Vol.23 No.6,772-775
[67] 刘芳 王涛 周登文,基于颜色一空间二维直方图的图象检索,计算机工程与应用,2002 No.12,85-88
[68] Tong S, Chang E. Support Vector Machine Active Learning for Image Retrieval. ACM Multimedia. Ottawa, Canada, 2001, 107-119.
[69] Laaksonen J, Koskela M, Oja E. PicSOM: Self-Organizing Maps for Content-Based Image Retrieval. Proceedings of International Joint Conference on NN. Washington, DC, 1999.
[70] Chiou-Ting Hsu Chuech-Yu Li, Relevance Feedback Using Generalized Bayesian Framework With Region-Based Optimization Learning, IEEE TRANSACTIONS ON IMAGE PROCESSING, 2005, VOL. 14, NO. 10,
[71] Yong Rui, Thomas S. Huang, Relevance Feedback: A Power Tool for Interactive Content-Based Image Retrieval, IEEE Trans on Circus and System for Video Technology, 1998
[72] Yong Rui, Thomas S. Huang, Content-Based image retrieval with relevance feedback in MARS, Proc of the IEEE Internet Confon Image Processing, New York: 1EEE Press, 1997
[73] 中家振,基于内容的图像检索技术研究,西北工业大学硕士学化论文,2005
[74] 黄翔宁,章毓晋.基于压缩域的图像检索技术研究进展,中国图像图开学报,2003,8(5):499～508.
[75] 沈兰荪,魏海,黄祥林.压缩域图像处理技术研究[J] .北京工业大学学报,2000,26(3):24～32。
[76] Idris F, Panchanathan S. Image indexing using vector quantization [A] . In: Proc. Of SPIE: Storage and Retrieval for Image and Video Databases[C] , San Jose, CA USA, 1995, 2420:373～380.
[77] Barbas J S, Wolk S I. Efficient organization of large ship radar databases using wavelets and structured vector quantization [A] .In: Prec. of A silomer Conference on Signals, Systems and Computers[C] , Pacific Grove, CA USA, 1993, 1:491～498.
[78] Vellaikal A, Kuo C C J, Dao S. Content-based retrieval of remotesensed images using vector quantization [A] . In: Proc. Of SPIE: Visual Information Processing IV[C] , San Jose, CA USA, 1995, 2488: 178～189.
[79] 王志勇,池哲儒,余英林.分形编码在图像检索中的应用[J] .电子学报,2000,28(6):19～23.
[80] Sloan A D. Retrieving database contents by image recognition : New fractal power[J] . Advanced Imaging, 1994, 9(5):5.
[81] Zhang A idong, Cheng Diao, Acharya Raj. Menon raghu, comparison of wavelet transform and fractal coding in texture basedimage retrieval[A] . In: Proc. of SPIE: VisualData Exploration and Analysis[C] , San Jose, CA USA, 1996, 2656: 116～125.
[82] Zhang Aidong, Cheng Diao, Acharya Raj. An apptoae.h to query by texture in image database systems[A] . In: Proc. of SPIE: Digital image Storage Archiving System[C] , San Jose, CA USA, 1995, 2606: 338～349.
[83] Stone H S, Li C S. Image matching by means of intensity and texture matching in the Fourier domain [A] ,.In: Proc. of SPIE: Storage and Retrieval for Image and Video Databases Ⅳ[C] , San Jose, CA USA, 1996, 2670: 337～349.
[84] Augusteijn M , Clemens L E, Shaw K A. Performance evaluation of texture measures for ground cover identification in satellite images by means of a neural network classifier[J] , IEEE Transactions on Geoscience Remote Sensing, 1995, 33 (3) : 616～626.
[85] Smith J R, Chang S E Transform features for texture classification and discrimination in large image databases[A] . In: Proc. of IEEE International, Conference. Image Processing[C] ,Austin, TXUSA, 1994, 3: 407～411.
[86] Reeves R, Kubik K, Osberger W. Texture characterization of compressed aerial images using DCT coefficients[A] . In: Proc.of SPIE: Storage and Retrieval for Image and Video DatabasesV[C] , San Jose, CA USA, 1997, 3022: 398～407.
[87] Shneier M, Mottaleb M A. Exploiting the JPEG compression scheme for image retrieval[J] . IEEE Transactions on Pattern Analysis and Machine Intelligence, 1096, 18(8) : 849～853.
[88] Sim D G, Kim H K, Park R H. Fast texture description and retrieval of DCT2based compressed images[J] , Electronic Letters,2001, 37(1): 18～19.
[89] Lay Jose A, Ling Guang. Image retrieval based on energy histograms of the low frequency DCT coefficients[A] . In: Proc. Of International Conference on Acoustics, Speech and Signal Processing[C] , Phoenix, Arizona, USA, 1999, 6: 3009～3012.
[90] Yu Hong Heather. Visual image retrieval on compressed domain with Qdistance[A] . In: Proceedings. Third International Conference on, Computational Intelligence and Multimedia Applications ICC IMA '99. [C] , New Delhi, USA, India, 1999: 285～289.
[91] Abdel-M alek A A, Hershey J E. Feature cueing in the discrete cosine domain[J] . Journal of Electronic Imaging, 1994, 3(1) : 71～80.
[92] Shen B, Sethi I K. Direct feature extraction from compressed images[A] . In: Proc. of SPIE: Storage and Retrieval for Image Video Databases Ⅳ[C] , San Jose, CA USA, 1996, 2670: 404～414.
[93] 黄祥林,沈兰荪.基于DCT压缩域的纹理图像分类,电子与信息学报,2002,24(2):216～221
[94] 黄祥林,宋磊,沈兰荪.基于DCT压缩域的图像检索方法,电子学报,2002,30(12):1786～1789
[95] Chang T, Kuo C C J. Texture analysis and classification with treestmctured wavelet transform[J] . IEEE Transactions on Image Processing, 1993, 2(4):429～441.
[96] Chen J L, Kundu A. Rotation and gray scale invariant texture identification using wavelet decomposition and hidden Markov model[J] . IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994, 16(2):208～214.
[97] Mandal M K, Aboulnasr T, Panchanathan S. Image indexing using moments and wavelets[J] , IEEE Transactions on Consumer Electronics, 1996, 42(3): 557～565.
[98] Manjunath B S, Ma W Y. Texture features for browsing and retrieval of image data[J] . IEEE Transactions. Pattern Analysis and Machine Intelligence, 1996, 18(8):837～841.
[99] Wang H, Chang S F. Adaptive image matching in the subband domain[A] . In: Proc. of SPIE: VCIP[C] , San Jose, CA USA, 1996, 2727: 885～896.
[100] Jacobs C E, Finkelstein A, Salesin D H. Fast multiresolution image querying[A] . In: Proc. of ACM SIGGRAPH Conference on Computer Graphics and Interactive Techniques[C] , LosAngeles, CA ,USA, 1995: 277～286.
[101] Froment J, Mallat S. Second generation compact image coding with wavelets[A] . In: C. K. Chui(Ed.), Wavelets: A Tutorial in Theory and Applications[M] , New York: Academic Press,1992.
[102] Lee Moon-Chuen,Pun Chi-Man.Texture classification using dominant wavelet packet energy features. In:Proceedings of IEEE Southwest Symposium on Image Analysis and Interpretation, Austin,TX,USA,2000.301～304
[103] Ma W Y, Manjunath B S.A comparison of wavelet transform features for texture image annotation. In: Proceedings of IEEE International Conference on Image Processing, Washington,DC, USA, 1995.256～259
[104] 李晓华,沈兰荪,基于压缩域的图像检索技术,计算机学报,2003.26(9):1051～1059
[105] 李晓华,沈兰荪,贾克斌,一种适用于网络应用的压缩图象快速检索方法[J] .电子学报,2002,30(12A):2016～2019
[2] M Stricker, M Orengo, Similarity of color images, Proc. SPIE: Storage and Retrieval for Image and Video Databases Ⅲ, 1995
[3] John R Smith, Shih-Fu Chang, Tools and techniques for color image retrieval, Proc. SPIE: Storage and Retrieval for Image and Video Database, 1995
[4] G Pass, R Zabih, Histogram refinement for content based image retrieval, IEEE Workshop on Applications of Computer Vision, 1996, pp. 96-102
[5] M Tuceryan, A K. lain, Texture analysis, Handbook of Pattern Recognition and Computer Vision, World Scientific;1993
[6] H Tamura, S Mod, T Yamawaki, Textural features corresponding to visual perception, IEEE Trans On Systems, Man and Cybermetics, 1978, Vol. Smc-8, No. 6
[7] Robert M Haralick, K Shanmugam, Textural features for image classification, IEEE Trans. On Sys, Man, and Cyb, 1973, SMC-3(6): 610-621
[8] H Zhang, D Zhong, A scheme for visual feature based image indexing, Proc. SPIE: Storage and Retrieval for Image and Video Databases Ⅲ, 1995
[9] T Chang, C C Jay Kuo, Texture analysis and classification with tree-structured wavelet transform, IEEE Trans. On Image Processing, 1993, Vol. 2, No. 4, pp. 429-441
[10] 徐曼,刘炜,韦志辉,基于内容的图像检索技术,计算机应用,2001,Vol.21,No.9
[11] 王涛,刘文印,孙家广等,傅立叶描述子识别物体的形状,计算机研究与发展,2002,Vol.39,No.12
[12] Yue ting Zhuang, Intelligent multimedia information analysis and retrieval with application to visual design, Ph. D thesis, Zhejiang University, 1998
[13] S Belognie, Shape Matching and Object Recognition Using Shape Contexts, IEEE Trans. Pattern Analysis and Machine Intelligence, 2002, Vol. 24, No. 24
[14] M K Hu, Visual pattern recognition by moment invariants, Computer methods in Image analysis, IEEE computer Society, 1977
[15] S K Chang, 0 Y Shi, C Y Yah, Iconic indexing by 2-D strings, IEEE Trans. Pattern Anal. Machine Intelligence, pp 413-428, 1987
[16] V N Gudivada, V V Raghavan, Design and evaluation of algorithms for image retrieval by spatial similarity, ACM Trans on Information Systems, 1995, Vol. 13, No. 2
[17] 梅承力,基于内容的图像检索关键技术研宄,上海交通大学博士论文,2001
[18] D White, R Jain, Similarity indexing: Algorithms and performance, Proc. SPIE: Storage and Retrieval for Image and Video Database, 1996
[19] G Salton, M J McGill, Introduction to Modem Information Retrieval, McGraw-Hill Book Company, New York, 1982
[20] 朱旭娟,一种基于压缩域图像检索系统开发,北京工业大学学位论文,2004
[21] Moses Charikar, Chandra Chekur, Incremental Clustering and Dynamic Information Retrieval, Proc of the 29th Annual ACM Symposium on Theory of Computing, 1997
[22] 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,2002.10
[23] Maria-Luiza, Anionic, Application of Data Mining Techniques for Medical Image Classification, 2nd International Workshop on Multimedia Data Mining, 2001
[24] AVailaya, H J Zhang, On Image Classification: City vs Landscape, IEEE CBAIVL, 1998
[25] W M Arnold, Marcel Worring, Content-Based Image Retrieval at the End of the Early Years, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, Vol. 22, No. 12
[26] 黄祥林,图像检索中的关键技术,测控技术,2002,Vol.21,No.5
[27] 黄祥林,沈兰荪,基于内容的图像检索技术研究,电子学报,2002,V01.30,No.7
[28] 胡军,耿国华,周明全,一个图像检索模型及其应用,西北大学学报(自然科学版),1999,Vol.29,No.6
[10] 徐曼,刘炜,韦志辉,基于内容的图像检索技术,计算机应用,V01.21,No.9,2001
[29] Myron Flickner, Harpreet Sawhney, Query by Image and Video Content: The QBIC System, IEEE, 1995
[30] John R Smith, Shih-Fu Chang, Visually Searching the Web for the Content, Columbia University, 1997
[31] A Pentland, R W Picard, S Sclaroff, Photobook: Content-Based Manipulation of Image Databases, Proc. SPIE: Storage and Retrieval Image and Video Database Ⅱ, 1994
[32] 黄元元,郭丽,杨静宇,基于主色调匹配的图像检索方法,计算机工程,2002,Vol.28,No.6
[33] 阴炳皓,赵臣,韩晓军,基丁改进的HSI空间模型的目标搜索方法,河北工业大学学报,2003,Vol.32,No.1
[34] Won Soon Kim, Rae Hong Park, Color Image Palette Construction based on the HSI ColorSystem for Minimizing the Reconstruction Error, IEEE Proceedings of International Conference on Image Processing, 1996
[35] 张全海,施鹏飞,基于HSV空间彩色图像的边缘提取方法,计算机仿真,2000,Vol.17,No.6
[36] 黎弘,翟战强,唐树才,利用ARC/INFO制作彩色晕渲图,计算机工程,1999,Vol.25,No.8
[37] 章毓晋,基于内容的视觉信息检索,北京:科学出版社,2003
[38] 龚声蓉,基于内容的图像检索方法研究,北京航空航天大学博士学位论文,2001
[39] 许金普,基丁内容的图像检索技术研究及其系统实现,天津师范大学研究生学位论文,2005
[40] Hemamalini Raman, Retrieval of Images In Digital Image Libraries, Department of Computer Science Curtin University of Technology
[41] A D Alexandrov, W Y Ma, Adaptive Filtering and Indexing for Image Databases, SPIE: Storage and Retrieval for Image and Video Databases HI, Vol. 2420
[42] Zhou Shaohua, Unsupervised Texture Segmentation Via Gaussian Markov Random Field(GMRF) Model and Filter Bank Decomposition, A project report for partial requirement of ENEE739J, Image Understanding, 2001
[43] 盛文,徐晨曦,杨江平,纹理分析窗大小的高斯.马尔可夫随机场模型估计方法,红外与激光工程,2000,Vol.29,No.6
[44] Smith J R, Chang S E Automated binary texture feature sets for image retrieval. Proceedings of IICAA, 1996, p2239-2242
[45] Mandal M K, Aboulnasr T, Fast wavelet histogram techniques for image indexing. Joumal of Computer Vision and Image Understanding,1999, 75(1),p99-110
[46] Lee Moon-Chuen. Texture classification using dominant wavelet packet energy features. Proceedings of ISSIA,2002, p301-304
[47] Mandal M K. Wavelet based coding and indexing of images and video[ph Ddissertation] , University of Ottawa, Canada,1998
[48] 吕娜,孙扬民,黄国丰,对图像检索应用概况的研究,情报科学,Vol.20,No.3,2002
[49] 赵汗青,沈佐锐,于新文,数学形态学在昆虫分类学上的应用研究—在目级阶元上的应用研究,昆虫学报.2003
[50] 商立群,基于zemike矩的图像识别,西安科技学院学报,2000,20(2):151～153
[51] 秦磊,金丰华,舒华忠,Zernike矩在基于内容的医学图像检索中的应用,计算机与现代化,2003,4:61～63
[52] Y.S. Kim, W.Y. Kim, Content-based trademark retrieval system using a visually salient feature, Image and Vision Computing 16(1998) 931-939
[53] Serge Belongie, Jitendra Malik, Jan Puzicha, Shape Matching and Objects Recognition Using Shape Contexts, IEEE Trans on Pattern Analysis and Machine Intelligence, 2002, Vol. 24, No. 24
[54] 王晓红、杨玲,基于颜色/形状直方图的图像检索方法,情报理论与实践 2003,4
[55] 王惠锋,孙正兴,王箭,语义图像检索研究进展,计算机研究与发展,2002,39(5):513～523
[56] Monika Gorkani and Rosalind W. Picard, Texture orientation for sorting photos at aglance, In Proc. lnt Conf. Pat. Rec,Volumel:459～464, Jerusalem,Israel,Oct, 1994.
[57] Szummer, M.;Picard, R.W. Content-Based Access of Image and Video Database, 1998. Proceedings., 1998 IEEE International Workshop on 3 Jan. 1998 Page(s):42-51
[58] A Vailaya, M Figueiredo, A K Jain etal. Image classification for content2based indexing. IEEE Trans on Image Processing, 2001, 10 (1) : 117～130
[59] 袁磊,曹奎,冯玉才,吴永英,一种基于LSI的图像语义检索技术,华中科技大学学报(自然科学版),2002,30(2):105～107
[60] Y Wu, Y T Zhuang, Y H Pan. Image retrieval system for Web: Webscope-CBIR. In: IEEE Proc of the llth Int'l Workshop on Database and Expert System s Applications. Greenwich, London, UK: IEEE CS Press 2000.620～624
[61] S Sclaroff, M L Cascia, L Taycher etal. Unifying textual and visual cues for content-based image retrieval on the World Wide Web. Computer Vision and Image Understanding, 1999,75(1):86～98
[62] 罗法 章毓晋 高永英,基于分析的图像有意义区域提取,计算机学报,2000,vol.23,No.12,1313-1319
[63] 吴冬升 吴乐南 黄波,基于小波模糊聚类区域分割的图像检索,信号处理,2002,vol.18,No.5,,422-426
[64] ByoungChul KO, Hae-Sung Lee, Hyeran Byun, Region-based Image Retrieval System Using Efficient Feature Description, Pattern Recognition, 2000. Proceedings. 15th International Conference on Barcelona, Spain, 2000, Volume 4, 283-286
[65] Mado A. Nascimento, Veena Sridhar, Xiaobo Li, Effective and efcient region-based image retrieval, Journal of Visual Languages and Computing, 2003, No.14, 151-179
[66] 王圆圆 丁志杰 万华林,基于视觉颜色聚类的彩色图像分割,北京理工大学学报,2003,Vol.23 No.6,772-775
[67] 刘芳 王涛 周登文,基于颜色一空间二维直方图的图象检索,计算机工程与应用,2002 No.12,85-88
[68] Tong S, Chang E. Support Vector Machine Active Learning for Image Retrieval. ACM Multimedia. Ottawa, Canada, 2001, 107-119.
[69] Laaksonen J, Koskela M, Oja E. PicSOM: Self-Organizing Maps for Content-Based Image Retrieval. Proceedings of International Joint Conference on NN. Washington, DC, 1999.
[70] Chiou-Ting Hsu Chuech-Yu Li, Relevance Feedback Using Generalized Bayesian Framework With Region-Based Optimization Learning, IEEE TRANSACTIONS ON IMAGE PROCESSING, 2005, VOL. 14, NO. 10,
[71] Yong Rui, Thomas S. Huang, Relevance Feedback: A Power Tool for Interactive Content-Based Image Retrieval, IEEE Trans on Circus and System for Video Technology, 1998
[72] Yong Rui, Thomas S. Huang, Content-Based image retrieval with relevance feedback in MARS, Proc of the IEEE Internet Confon Image Processing, New York: 1EEE Press, 1997
[73] 中家振,基于内容的图像检索技术研究,西北工业大学硕士学化论文,2005
[74] 黄翔宁,章毓晋.基于压缩域的图像检索技术研究进展,中国图像图开学报,2003,8(5):499～508.
[75] 沈兰荪,魏海,黄祥林.压缩域图像处理技术研究[J] .北京工业大学学报,2000,26(3):24～32。
[76] Idris F, Panchanathan S. Image indexing using vector quantization [A] . In: Proc. Of SPIE: Storage and Retrieval for Image and Video Databases[C] , San Jose, CA USA, 1995, 2420:373～380.
[77] Barbas J S, Wolk S I. Efficient organization of large ship radar databases using wavelets and structured vector quantization [A] .In: Prec. of A silomer Conference on Signals, Systems and Computers[C] , Pacific Grove, CA USA, 1993, 1:491～498.
[78] Vellaikal A, Kuo C C J, Dao S. Content-based retrieval of remotesensed images using vector quantization [A] . In: Proc. Of SPIE: Visual Information Processing IV[C] , San Jose, CA USA, 1995, 2488: 178～189.
[79] 王志勇,池哲儒,余英林.分形编码在图像检索中的应用[J] .电子学报,2000,28(6):19～23.
[80] Sloan A D. Retrieving database contents by image recognition : New fractal power[J] . Advanced Imaging, 1994, 9(5):5.
[81] Zhang A idong, Cheng Diao, Acharya Raj. Menon raghu, comparison of wavelet transform and fractal coding in texture basedimage retrieval[A] . In: Proc. of SPIE: VisualData Exploration and Analysis[C] , San Jose, CA USA, 1996, 2656: 116～125.
[82] Zhang Aidong, Cheng Diao, Acharya Raj. An apptoae.h to query by texture in image database systems[A] . In: Proc. of SPIE: Digital image Storage Archiving System[C] , San Jose, CA USA, 1995, 2606: 338～349.
[83] Stone H S, Li C S. Image matching by means of intensity and texture matching in the Fourier domain [A] ,.In: Proc. of SPIE: Storage and Retrieval for Image and Video Databases Ⅳ[C] , San Jose, CA USA, 1996, 2670: 337～349.
[84] Augusteijn M , Clemens L E, Shaw K A. Performance evaluation of texture measures for ground cover identification in satellite images by means of a neural network classifier[J] , IEEE Transactions on Geoscience Remote Sensing, 1995, 33 (3) : 616～626.
[85] Smith J R, Chang S E Transform features for texture classification and discrimination in large image databases[A] . In: Proc. of IEEE International, Conference. Image Processing[C] ,Austin, TXUSA, 1994, 3: 407～411.
[86] Reeves R, Kubik K, Osberger W. Texture characterization of compressed aerial images using DCT coefficients[A] . In: Proc.of SPIE: Storage and Retrieval for Image and Video DatabasesV[C] , San Jose, CA USA, 1997, 3022: 398～407.
[87] Shneier M, Mottaleb M A. Exploiting the JPEG compression scheme for image retrieval[J] . IEEE Transactions on Pattern Analysis and Machine Intelligence, 1096, 18(8) : 849～853.
[88] Sim D G, Kim H K, Park R H. Fast texture description and retrieval of DCT2based compressed images[J] , Electronic Letters,2001, 37(1): 18～19.
[89] Lay Jose A, Ling Guang. Image retrieval based on energy histograms of the low frequency DCT coefficients[A] . In: Proc. Of International Conference on Acoustics, Speech and Signal Processing[C] , Phoenix, Arizona, USA, 1999, 6: 3009～3012.
[90] Yu Hong Heather. Visual image retrieval on compressed domain with Qdistance[A] . In: Proceedings. Third International Conference on, Computational Intelligence and Multimedia Applications ICC IMA '99. [C] , New Delhi, USA, India, 1999: 285～289.
[91] Abdel-M alek A A, Hershey J E. Feature cueing in the discrete cosine domain[J] . Journal of Electronic Imaging, 1994, 3(1) : 71～80.
[92] Shen B, Sethi I K. Direct feature extraction from compressed images[A] . In: Proc. of SPIE: Storage and Retrieval for Image Video Databases Ⅳ[C] , San Jose, CA USA, 1996, 2670: 404～414.
[93] 黄祥林,沈兰荪.基于DCT压缩域的纹理图像分类,电子与信息学报,2002,24(2):216～221
[94] 黄祥林,宋磊,沈兰荪.基于DCT压缩域的图像检索方法,电子学报,2002,30(12):1786～1789
[95] Chang T, Kuo C C J. Texture analysis and classification with treestmctured wavelet transform[J] . IEEE Transactions on Image Processing, 1993, 2(4):429～441.
[96] Chen J L, Kundu A. Rotation and gray scale invariant texture identification using wavelet decomposition and hidden Markov model[J] . IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994, 16(2):208～214.
[97] Mandal M K, Aboulnasr T, Panchanathan S. Image indexing using moments and wavelets[J] , IEEE Transactions on Consumer Electronics, 1996, 42(3): 557～565.
[98] Manjunath B S, Ma W Y. Texture features for browsing and retrieval of image data[J] . IEEE Transactions. Pattern Analysis and Machine Intelligence, 1996, 18(8):837～841.
[99] Wang H, Chang S F. Adaptive image matching in the subband domain[A] . In: Proc. of SPIE: VCIP[C] , San Jose, CA USA, 1996, 2727: 885～896.
[100] Jacobs C E, Finkelstein A, Salesin D H. Fast multiresolution image querying[A] . In: Proc. of ACM SIGGRAPH Conference on Computer Graphics and Interactive Techniques[C] , LosAngeles, CA ,USA, 1995: 277～286.
[101] Froment J, Mallat S. Second generation compact image coding with wavelets[A] . In: C. K. Chui(Ed.), Wavelets: A Tutorial in Theory and Applications[M] , New York: Academic Press,1992.
[102] Lee Moon-Chuen,Pun Chi-Man.Texture classification using dominant wavelet packet energy features. In:Proceedings of IEEE Southwest Symposium on Image Analysis and Interpretation, Austin,TX,USA,2000.301～304
[103] Ma W Y, Manjunath B S.A comparison of wavelet transform features for texture image annotation. In: Proceedings of IEEE International Conference on Image Processing, Washington,DC, USA, 1995.256～259
[104] 李晓华,沈兰荪,基于压缩域的图像检索技术,计算机学报,2003.26(9):1051～1059
[105] 李晓华,沈兰荪,贾克斌,一种适用于网络应用的压缩图象快速检索方法[J] .电子学报,2002,30(12A):2016～2019