图像特征提取及基于内容图像数据库检索理论和方法研究
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摘要
图像数据库检索是当今信息时代人们广泛关注的热点问题，主要包括对图像内容的描述(特征表达及提取)、图像数据库管理、图像匹配等内容。本文以图像数据库检索为主线，讨论了基于视觉内容的图像检索方法，提出包括基于区域颜色直方图、灰度—基元共生矩阵及向心矩比、偏心矩比、惯性矩比的特征描述方式；对遗传算法存在的早熟、收敛到最优解慢等问题提出了解决方法，并将改进遗传算法应用到图像分割中，编制了相应程序。
颜色特征是描述图像的最常用方法，颜色直方图利用图像颜色的比例分布能够较好地反映图像颜色特征。但是因为颜色直方图很难体现图像的空间信息，会出现同一直方图对应多幅图像问题。本文提出的基于区域块的颜色直方图技术，首先将目标集中在图像的中间区域，再将图像以金字塔结构形式进行分块。通过比较各个区域颜色直方图的相似性匹配图像，可克服全局直方图的局限性，对颜色直方图的度量是其中一个关键技术，利用惯性比来从颜色直方图提取特征，能够保证简单明了地表达颜色直方图。
对富含纹理图像，采用纹理特征表达会更有效，颜色的二维统计方式—灰度共生矩阵是常用方法。对于结构明显的纹理图像可以采用结构方法描述图像特征。本文将统计方法与结构方法有机地结合起来，构建了纹理基元，提出了描述纹理图像的灰度—基元共生矩阵，从这个矩阵中提取用以描述图像的特征值，并组成检索图像的特征向量。
图像分割是解决目标检测、特征提取和目标识别等问题的关键所在，形状特征提取的首要任务就是获得目标图像边界。利用遗传算法对图像能够实现单阈值和多阈值分割。本文首先对传统遗传算法进行了改进，提出了适应函数标定公式，定义了相似度概念。这样可以增加群体多样性，在一定程度上避免早熟现象发生；然后利用改进遗传算法对图像进行了分割计算，将目标与背景分割开来。
形状特征是对图像中边界清晰的目标的最好表达方式。但是即使是找到了目标轮廓，如何来有效地表示该轮廓也是非常困难的。本文提出向心矩比、偏心矩比、惯性矩比概念，对线性化后的轮廓应用本文方法，既能较好地描述图像目标，又具有旋转、平移和尺度不变性。
支持向量机(SVM)是近年来出现的分类方法。本文将SVM及其改进算法SMO应用到图像检索中，可以完成整类检索任务。因为SVM只能够用于两类分类问题，本文构建了二叉树分类器，将SVM技术扩展到多类别分类领域当中；利用前述的图像颜色特征、纹理特征和形状特征作为输入向量，通过训练得到支持向量，构建多类分类函数，进行图像检索。
图像数据库检索系统应能够完成检索、图像数据管理等任务。本文利用VB6.0
编制了检索原型系统，包括有特征提取、数据库管理、匹配等模块，采用了基于文
本、颜色、纹理、形状和SVM分类检索方法。实验结果表明，本文提出的图像颜
色特征、纹理特征和形状特征能够满足对图像内容的描述要求；将支持向量机分类
技术应用到图像数据库检索能够得到较好的检索效果。
Image database retrieval is a hot topic and has attracted increased attention from researchers. It includes several contents such as describing the image visual content (extracting features), image database management, image matching and so on. This dissertation deals with the content-based image retrieval (CBIR) theory and technique; some new features and tools for more concisely and discriminatingly charactering the content of an image are proposed, such as region-based color histogram, grey-primitive co-occurrence matrix, ratio of centripetal moment, ratio of eccentric moment and ratio of inertial moment. A new modified genetic algorithm is also described in this dissertation, which can upgrade the performance of standard genetic algorithm (SGA) while used in image segmentation.
Color feature is often used to describe image content. Color histogram built from cumulative distributions of content colors is a main color feature using in image retrieval. However, the color histogram does not contain the information of spatial distribution of colors across an image. So there might be different images which possess different contents but share with same color histogram. To deal with this problem effectively, a region-based color histogram (RBCH) approach is proposed in this dissertation. In RBCH, an image is first decomposed into several subregions(blocks) with pyramid data structure, and the subregion color histograms are built correspondingly. After that, an integrated color histogram is built based on the subregion color histograms. This integrated color histogram overcomes the disadvantage of simple global color histogram. To digitally represent a color histogram an inertial moment ratio calculated from a color histogram graph is proposed as a new digital feature of the color histogram.
It is more efficient to use texture feature to describe an image with complicated textural contents. Grey co-occurrence matrix, which is a 2D color statistical feature, is often used to analyze such images. For the vivid textural structure images it had better use the structural approach to characterize image content. In this dissertation a new statistical tool called as grey-primitive co-occurrence matrix is proposed, which is based on predefined specific primitive texture structures. The features extracts from a set of this type matrix can be formed as a primitive texture feature vector and used in retrieving images from image database. The grey-primitive co-occurrence matrix based approach provides better performance than conventional grey co-occurrence matrix based approach.
Image segmentation is very important in object detection, feature extraction and object recognition processing. Object outline detection must be done before shape features are extracted. Recently single-threshold or multi-threshold is often used to segment image and detect object contour on an image by means of genetic algorithm. A modified genetic algorithm is proposed. In the dissertation a different fitness function is
constructed and similarity is introduced, which can increase variety of population md avoid prematurity.
Shape feature is the most suitable tool in characterizing image content object which has clear object outline. Even an object contour has been extracted, it is still difficult in constructing suitable shape features to describe the shape discriminatingly and efficiently. Some new features such as ratio of centripetal moment, ratio of eccentric moment and ratio of inertial moment are introduced in this dissertation. These features have the properties of scale-invariance-, rotation-invariance and translation-invariance, and can be used well in depicting the linearized outline object.
Support Vector Machine (SVM) is a new classification method. SVM and its implementation technique SMO can be used for image retrieval; however, they were originally developed for dealing with two-classification problem, so a binary tree and a matrix for classification are constructed for solving multi-classification problem. Feature vectors
颜色特征是描述图像的最常用方法，颜色直方图利用图像颜色的比例分布能够较好地反映图像颜色特征。但是因为颜色直方图很难体现图像的空间信息，会出现同一直方图对应多幅图像问题。本文提出的基于区域块的颜色直方图技术，首先将目标集中在图像的中间区域，再将图像以金字塔结构形式进行分块。通过比较各个区域颜色直方图的相似性匹配图像，可克服全局直方图的局限性，对颜色直方图的度量是其中一个关键技术，利用惯性比来从颜色直方图提取特征，能够保证简单明了地表达颜色直方图。
对富含纹理图像，采用纹理特征表达会更有效，颜色的二维统计方式—灰度共生矩阵是常用方法。对于结构明显的纹理图像可以采用结构方法描述图像特征。本文将统计方法与结构方法有机地结合起来，构建了纹理基元，提出了描述纹理图像的灰度—基元共生矩阵，从这个矩阵中提取用以描述图像的特征值，并组成检索图像的特征向量。
图像分割是解决目标检测、特征提取和目标识别等问题的关键所在，形状特征提取的首要任务就是获得目标图像边界。利用遗传算法对图像能够实现单阈值和多阈值分割。本文首先对传统遗传算法进行了改进，提出了适应函数标定公式，定义了相似度概念。这样可以增加群体多样性，在一定程度上避免早熟现象发生；然后利用改进遗传算法对图像进行了分割计算，将目标与背景分割开来。
形状特征是对图像中边界清晰的目标的最好表达方式。但是即使是找到了目标轮廓，如何来有效地表示该轮廓也是非常困难的。本文提出向心矩比、偏心矩比、惯性矩比概念，对线性化后的轮廓应用本文方法，既能较好地描述图像目标，又具有旋转、平移和尺度不变性。
支持向量机(SVM)是近年来出现的分类方法。本文将SVM及其改进算法SMO应用到图像检索中，可以完成整类检索任务。因为SVM只能够用于两类分类问题，本文构建了二叉树分类器，将SVM技术扩展到多类别分类领域当中；利用前述的图像颜色特征、纹理特征和形状特征作为输入向量，通过训练得到支持向量，构建多类分类函数，进行图像检索。
图像数据库检索系统应能够完成检索、图像数据管理等任务。本文利用VB6.0
编制了检索原型系统，包括有特征提取、数据库管理、匹配等模块，采用了基于文
本、颜色、纹理、形状和SVM分类检索方法。实验结果表明，本文提出的图像颜
色特征、纹理特征和形状特征能够满足对图像内容的描述要求；将支持向量机分类
技术应用到图像数据库检索能够得到较好的检索效果。
Image database retrieval is a hot topic and has attracted increased attention from researchers. It includes several contents such as describing the image visual content (extracting features), image database management, image matching and so on. This dissertation deals with the content-based image retrieval (CBIR) theory and technique; some new features and tools for more concisely and discriminatingly charactering the content of an image are proposed, such as region-based color histogram, grey-primitive co-occurrence matrix, ratio of centripetal moment, ratio of eccentric moment and ratio of inertial moment. A new modified genetic algorithm is also described in this dissertation, which can upgrade the performance of standard genetic algorithm (SGA) while used in image segmentation.
Color feature is often used to describe image content. Color histogram built from cumulative distributions of content colors is a main color feature using in image retrieval. However, the color histogram does not contain the information of spatial distribution of colors across an image. So there might be different images which possess different contents but share with same color histogram. To deal with this problem effectively, a region-based color histogram (RBCH) approach is proposed in this dissertation. In RBCH, an image is first decomposed into several subregions(blocks) with pyramid data structure, and the subregion color histograms are built correspondingly. After that, an integrated color histogram is built based on the subregion color histograms. This integrated color histogram overcomes the disadvantage of simple global color histogram. To digitally represent a color histogram an inertial moment ratio calculated from a color histogram graph is proposed as a new digital feature of the color histogram.
It is more efficient to use texture feature to describe an image with complicated textural contents. Grey co-occurrence matrix, which is a 2D color statistical feature, is often used to analyze such images. For the vivid textural structure images it had better use the structural approach to characterize image content. In this dissertation a new statistical tool called as grey-primitive co-occurrence matrix is proposed, which is based on predefined specific primitive texture structures. The features extracts from a set of this type matrix can be formed as a primitive texture feature vector and used in retrieving images from image database. The grey-primitive co-occurrence matrix based approach provides better performance than conventional grey co-occurrence matrix based approach.
Image segmentation is very important in object detection, feature extraction and object recognition processing. Object outline detection must be done before shape features are extracted. Recently single-threshold or multi-threshold is often used to segment image and detect object contour on an image by means of genetic algorithm. A modified genetic algorithm is proposed. In the dissertation a different fitness function is
constructed and similarity is introduced, which can increase variety of population md avoid prematurity.
Shape feature is the most suitable tool in characterizing image content object which has clear object outline. Even an object contour has been extracted, it is still difficult in constructing suitable shape features to describe the shape discriminatingly and efficiently. Some new features such as ratio of centripetal moment, ratio of eccentric moment and ratio of inertial moment are introduced in this dissertation. These features have the properties of scale-invariance-, rotation-invariance and translation-invariance, and can be used well in depicting the linearized outline object.
Support Vector Machine (SVM) is a new classification method. SVM and its implementation technique SMO can be used for image retrieval; however, they were originally developed for dealing with two-classification problem, so a binary tree and a matrix for classification are constructed for solving multi-classification problem. Feature vectors
引文
[1] 牛憨笨．图像信息获得技术研究进展．深圳大学学报，2000，17 (4)：1～10
[2] Volker Roth. Content-based Retrieval from Digital Video. Image and Vision Computing 17 (1999): 531-540
[3] W Niblack, R Barber. The QBIC Project: Querying Images by Content Using Color, Texture, and Shape. In Storage and Retrieval for Image and Video Databases, SPIE, 1993, Feb, Vol.1908: 173～187
[4] Myron Flickner, Harpreef Sawhney. Query by Image and Video Content: the QBIC System. IEEE Computer, 1995, September: 23～32
[5] http://vrw.excalib.com:8015/cst
[6] Cox I., Miller M. PicHunter: Bayesian Relevance Feedback for Image Retrieval. In: Proceedings of the International Conference on Pattern Recognition. Vienna Austria, 1996:361～369
[7] Ma W Y, Manjunath B S. Texture Features and Learning Similarity. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, June 1996:425～430
[8] Rohini Srihari, Zhangfei Zhang and Aibing Rao. Intelligent Indexing and Semantic Retrieval of Multimodal Documents. Information retrieval, 2000, 2 (2): 245～275
[9] Rui Y, Huang T S. Content-Based Image Retrieval with Relevance Feedback in MARS. In: Proceedings of the IEEE International Conference on Image Processing Santa Barbara, California, October 1997: 815～818
[10] Peng J, Bhanu B. Probabilistic Feature Relevance Learning for Content-Based Image Retrieval. Computer Vision and Image Understanding 75 (1/2), 1999: 150～164
[11] Jing Peng, Bir Bhanu. Independent Feature Analysis for Image Retrieval. Pattern Recognition Letters, 22 (2001): 63～74
[12] 章毓晋编著．图象工程(上册)：图象处理和分析．清华大学出版社，1999
[13] 贾云得编著．机器视觉．科学出版社，2000
[14] Swain M J, Ballard D H. Color Indexing. int. J. Computer Vision, 1991, 7 (1): 11～32
[15] Carlo Columbo, Alberto Del Bimbo. Color-Induced Image Representation and Retrieval. Pattern Recognition, 32 (1999): 1685～1695
[16] Tao Chen, Li-Hui Chen. Fast Mapping Algorithm for Histogram to Binary Set Conversion. Pattern Recognition Letters, 21 (2000): 899～906
[17] 刘忠伟，章毓晋．利用局部累加直方图进行彩色图象检索．中国图象图形学报，1998，3(7)：533～537
[18] Sawhney H S, Hafner J L. Efficient Color Histogram Indexing. IEEE International Conference on Image Processing, 1994, 2:66～70
[19] Stricker M, Swan M. The Capacity of Color Histogram Indexing. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1994: 704～708
[20] B M Mehtre, M S Kankanhalli. Color Matching for Image Retrieval. Pattern Recognition Letters, 16 (1995): 325～331
[21] S C Pie, C M Cheng. Extracting Color Feature and Dynamic Matching for Image DataBase Retrieval. IEEE Trans. Circuits Syst. Video Technol, 1999, 9 (3): 501～512
[22] L Cinque, G Ciocca, S Levialdi. Color-Based Image Retrieval Using Spatial-Chromatic Histograms. Image and Vision Computing, 19 (2001): 979～986
[23] M Stricker, A Dimai. Color Indexing With Weak Spatial Constraints. SPIE Proceedings. 1996, 2670: 29～40
[24] J R Smith, F S Chang. Tools and Techniques for Color Image Retrieval, Symposium on Electronic Imaging: Science and Technology-Storage and Retrieval for Image and Video Database IV, Vol. 2670, 1996: 426～237
[25] Nicu Sebe, Michael S Lew. Color-Based Retrieval. Pattern Recognition Letters, 22 (2001): 223～230
[26] Yu Zhong, Anil K. Jain. Object Localization Using Color, Texture and Shape. Pattern Recognition, 33(2000): 671～684
[27] Ji Yeun Kim. Color Indexing Using Chromatic Invariant. Pattern Recognition, 34 (2001): 1189～1197
[28] E Binaghi, I Gagliardi. Image Retrieval Using Fuzzy Evaluation of Color Similarity. Int. J. Pattern Recognition Artificial Intelligence, 1994, 8: 945～968
[29] Y Rubner, C Tomasi. A Metric for Distributions with Applications to Image Database. Proc. of the IEEE Int. Conf. on ComputerVision, 1998: 59～66
[30] Venkat N Gudivada. Content-Based Image Retrieval System. Computer, 1995, September, 18～22
[31] 金韬，任秀丽．图像检索中颜色特征的提取与匹配．计算机辅助设计与图形学报，2000，12 (6)：459～462
[32] Jia-Guu Leu. On Indexing the Periodicity of Image Textures. Image and Vision Computing, 19(2001): 987～1000
[33] Haralick R M, Shangmugam, Dinstein. Textural Feature for Image Classification. IEEE Trans on System, Man, Gybernetics, 1973, SMC-3 (6): 610～621
[34] 洪继光．灰度-梯度共生矩阵纹理分析方法．自动化学报．1984，10 (1)：22～25
[35] A Drimbarean, P F Whelan. Experiments in Colour Texture Analysis. Pattern Recognition Letters, 22 (2001): 1161～1167
[36] George Paschos. Fast Color Texture Recognition Using Chromaticity Moments. Pattern Recognition Letters, 21 (2000): 837～841
[37] Hao He, Yan Qiu Chen. Unsupervised Texture Segmentation Using Resonance Algorithm for Natural Scenes. Pattern Recognition Letters, 21 (2000): 741～757
[38] Jiebo Luo. Self-supervised Texture Segmentation Using Complementary Types of Features. Pattern Reconition, 34 (2001): 2071～2082
[39] Timo Ojala, Mattti Piekinen. Unsupervised Texture Segmentation Using Feature Distributions. Pattern Recognition, 32 (1999): 477～486
[40] http://deecom03.poliba.it/DrawSearch/Drawsearch.html
[41] Smith J R, Chang S F. Transform Feature for Texture Classification and Discrimination in Large Image Database. In: Proc of IEEE Int'l Conf on Image Processing. Austin, Texas, 1994
[42] 赵健，张文军．一种有效的基于小波帧变换包的纹理分类方法．计算机辅助设计与图形学学报，1997，9 (6)：520～526
[43] Wen-Shou Chou. Classifying Image Pixels into Shaped, Smooth, and Textured Points. Pattern Recognition, 32 (1999): 1697～1706
[44] Kalle Karu, Anil K Jain, Is There Any Texture in the Image. Pattern Recognition, 1996, 29 (9):
1437-1446
[45] Dmitry Chetverikov. Texture Analysis Using Feature-Based Pairwise Intersection Maps. Pattern recognition, 32 (1999): 487～502
[46] Damian G Jones, Paul T Jackway. Granolds: A novel Texture Representation. Pattern Recognition, 33 (2000): 1033～1045
[47] S Baheerathan, F Albregtsen. New Texture Features Based on the Complexity Curve. Pattern Recognition Letters, 32 (1999): 605～618
[48] David A Clausi, M Ed Jernigan. Designing Gabor Filters for Optimal Texture Separability. Pattern Recognition 33 (2000): 1835～1840
[49] M Pietikinen, T Ojala, Z Xu. Rotation-invariant Texture Classification using Feature Distributions. Pattern Recognition, 33 (2000): 43～52
[50] Richard E Frye, Robert S Ledley. Texture Discrimination Using Discrete Cosine Transformation Shift-insensitive(DCTSIS)] Descriptors. Pattern Recognition, 33(2000): 1585～1598
[51] 丁险峰，吴洪，张宏江，马颂德．形状匹配综述．自动化学报，2001，27 (5)：678～694
[52] Rajiv Mehrotra, James E. Gray. Similar-Shape Retrieval in Shape Data Management. IEEE Computer, September 1995:57～62
[53] Freeman H. On the Encoding of Arbitrary Geometric Configurations. IRE Trans, 1961, 10: 260～268
[54] Freeman H. Computer Processing of Line Drawing Images. ACM Computing Surveys, 1974, 6: 57～97
[55] Freeman H. Shape Description via the Use of Critical Points. Pattern Recognition, 1978, 10: 159～166
[56] Freeman H. Comparative Analysis of Line Drawing Modeling schemes. Computer Graphics Image Process, 1980, 12: 203～223
[57] Terry Caelli, David Reye. On the Classification of Image Regions by Color, Texture and Shape. Pattern Recognition, 1993, 26 (4): 461～470
[58] Hu M K. Pattern Recognition by Moment Invariant. In: Proc. IRE, 1961, 49: 1428
[59] Keping Chen. Efficient Parallel Algorithms for the Computation of Two-Dimensional Image Moments. Pattern Reconition, 1990, 23 (1/2): 109～119
[60] Miao Zhenjiang. Zernike Moment-based Image Shape Analysis and its Application. Pattern Recognition Letters, 21 (2000): 169～177
[61] CHO-HAK THE and Roland T. CHIN. On Image Analysis by the Methods of Moments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1988, 10 (4): 496～513
[62] Moment-Based Image-Normalization with High Noise-Tolerance. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1997, 19 (2): 136～139
[63] Alexander G Mamistvalov. N-Dimensional Moment Invariants and Conceptual Mathematical Theory of Recognition n-Dimensional Solids. IEEE Transaction on Pattern Analysis and Intelligence, 1998, 20 (8): 819～831
[64] Jain A K, Vailaya. Shape-Based Retrieval: A Case Study with Trademark Image Database. Pattern Recognition, 1998, 31 (9): 1369～1390
[65] Yang L, Algregtsen. A Fast Computation of Invariant Geometric Moments: A New Method Giving Correct Results. In: Proc of IEEE Int'l Conf on Image Processing. Austin Texas, 1994
[66] Kapur D, Lakshman Y N, Saxena T. Computing Invariant Using Elimination Methods, In: Proc of Int'1 Sysposium on Computer Vision. Florida, 1995: 97～102
[67] Ivar Balslev, Kasper Dring. Weighted Central Moments in Pattern Recognition. Pattern Recognition Letters, 21 (2000): 381～384
[68] Zhan C T, Roskies R Z. Fourier Descriptors for Plane Closed Curves. IEEE Trans. Computer, 1972, 21 (3): 269～281
[69] Rui Y, Alfred C, Huang T S. Modified Fourier Descriptor for Shape Representation, a Practical Approach. In: Proc. of First Int'1 Workshop on Image Database and Multimedia. Search, 1996
[70] Chen Guangyi, Tien D Bui. Invariant Fourier Wavelet Descriptor for Pattern Recognition. Pattern Recognition, 1999, 32 (7): 1083～1088
[71] Tieng Q M, Boles W W. Recognition of 2 D Object Contours Using the Wavelet Transform Zero- Crossing Representation. IEEE Trans. PAMI, 1997, 19 (8): 910～916
[72] 杨翔英，章毓晋．小波轮廓描述符及在图像查询中的应用．计算机学报，1999，22 (7)：752～757
[73] S Volmer. Tracing Images in Large Databases by Comparison of Wavelet Fingerprints. In Proceedings of the 2nd International Conference on Visual Information Systems, San Diego, 1997, 12: 163～172
[74] Ansari N, Delp E J. On the Distribution of a Deforming Triangle. Pattern Recognition, 1990, 23 (12): 1333～1341
[75] Young Susan S, Scott Peter D, Nasrabadi Nasser M. Object Recognition Using Multiplayer Hopfield Neural Network. IEEE Trans. Image Processing, 1997, 6 (3): 357～372
[76] Rajpal Navi, Chaudhury Stantanu, Banerjee Subhashis. Recognition of Partially Occluded Objects Using Neural Network Based Indexing. Pattern Recognition, 1999, 32:1737～1749
[77] http://attrasoft.com/abm34.html
[78] Anil K. Jain, Yu Zhong. Object Matching Using Deformable Templates. IEEE Transactions on Pattern Analysi and Machine Intelligence, 1996, 18 (3): 267～277
[79] Alberto Del Bimbo, Pietro. Visual Image Retrieval by Elastic Matching of User Sketches. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1997, 19 (2): 121～131
[80] P Pala, S Santini. Image Retrieval by Shape and Texture. Pattern Recognition, 32 (1999): 517～527
[81] J M Fuertes, M Lucena. A Scheme of Color Image Retrieval from Databases. Pattern Recognition Letters, 22 (2001): 323～337
[82] 杨新，郭丰俊等．一类图象匹配方程的有限差分解法．上海交通大学学报，1998，32 (9)：53～56
[83] 周杰，卢春雨等．人脸自动识别方法综述．电子学报，2000，28 (4)：102～106
[84] http://www.ee.surrey.ac.uk/Research/VSSP/imagedb/squid.html
[85] Anil K Jain, Aditya Vailaya. Image Retrieval Using Color and Shape. Pattern Recognition, 1996, 29 (8): 1233～1244
[86] Ravikanth Malladi, James A Sthian. A Unified Approach to Noise Removal, Image Enhancement, and Shape Recovery. IEEE Transaction on Image Processing, 1996, 5 (11): 1554～1568
[87] 邬浩，潘云鹤等．基于对象形状的图象检索查询技术．软件学报，1998，9 (5)：343～349
[88] Yung-Kuan Chan, Chin-Chen Chang. Image Matching Using Run-Length Feature. Pattern Recognition Letter, 22 (2001): 447～455
[89] Hirobumi Nishida. Structural Shape Indexing with Feature Generation Models. Computer Vision and Image Understanding, 1999, 73 (1): 121～136
[90] Pentland, Picard R W, Sclaroff S. Photo Book: Tools for Content-Based Manipulation of Image
Database. International Journal of Computer Vision, 1996, 18 (3)
[91] J Serra. Image Analysis and Mathematical Morphology. Theoretical Advances. Academic Press, London, 1988, 2
[92] Haralick R M, Shapiro L G. Computer and Robot Vision. 1992, Vol.1, Addison-Wesley
[93] Vincent L. Morphological Greyscale Reconstruction in Image Analysis: Applications and Efficient Algorithms. IEEE Trans. On Image Processing, 1993, 2 (2): 176～201
[94] 崔屹．图像处理与分析—数学形态学方法及应用．北京：科学出版社，2000
[95] Cirmmons T, Brown W. Image Algebra and Automatic Shape Processing. IEEE Trans. Aerospace and Electronic Systems, January, 1985, 221
[96] Bhattacharya P, Zhu W. Shape Recognition Method Using Morphological Hit-or Miss Transform. Optical Engineering, 1995, 34 (6): 1728～1725
[97] Computing Geometric Moments Using Morphological Erosions. Pattern recognition 34 (2001): 271～276
[98] A Hill, C J Taylor. Model-Based Image Interpretation Using Genetic Algorithms. Image and Vision Computing. 1992, 10 (5): 295～300
[99] Michalalewicz Z. Genetic Algorithm+Data Structure=Evolution Programs, 2nd ed., Springer-Verlag, New York, 1994
[100] Grefenstette J J. Optimization of Control Parameters for Genetic Algorithms. IEEE Trans. On Syst., Man & Cybern, 1986, 16 (1): 122～128
[101] Goldberg D E. Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA: Addition-Wesley, 1989
[102] 石琳珂，孙铭心等．地球物理遗传反演方法．北京：地震出版社，2000
[103] 徐宗本，高勇．遗传算法过早收敛现象的特征分析及其预防．中国科学，1996，26 (4)：564～375
[104] Davis L. Handbook of Genetic Algorithms. New York: Van Nostrand Reinhold, 1991
[105] Srinivas M, Patnaik L M. Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms. IEEE Trans. Syst Man and Cyber, 1994, 24 (4): 656～667
[106] 宋爱国，陆佶人．一种基于排序操作的优化算子自适应遗传算法．电子学报，1999，27 (1)：85～88
[107] Shu-Chien Huang, Yung-Nien Sun. Polygonal Approximation Using Genetic Algorithms. Pattern Recognition, 32 (1999): 1409～1420
[108] Shiu Yin Yuen, Chi Ho Ma. Genetic Algorithm with Competitive Image Labeling and Least Square. Pattern Recognition, 33 (2000): 1949～1966
[109] 张元亮，郑南宁等．基于遗传算法的混合分形编码．自动化学报，1999，25 (1)：142～144
[110] 陈宁江，李介谷等．遗传算法在立体图象匹配中的应用．上海交通大学学报，1998，32 (9)：49～52
[111] Motohide Yoshimura, Shunichiro Oe. Evolutionary Segmentation of Texture Image Using Genetic Algorithms towards Automatic Design of Optimum Number of Segmentation Areas. Pattern Recognition, 32 (1999): 2041～2054
[112] Vapnik V, Lerner A. Pattern Recognition Using Generalized Portrait. Automation and Remote Control. 1963, 24 (6): 774～780
[113] Pierre M L, Drezet, Robert F Harrison. A New Method for Sparsity Control in Support Vector Classification and Regression. Pattern Recognition 34 (2001): 111～125
[114] David M J Tax, Robert P W Duin. Support Vector Domain Description. Pattern
Recognition Letters, 20 (1999): 1191～1199
[115] Joao Pedro Pedroso. Support Vector Machines with Different Norms: Motivation, Formulations and Results. Patter Recognition Letters, 22 (2001): 1263～1272
[116] S Amari, S Wu. Improving Support Vector Machine Classifiers by Modifying Kernel Functions. Neural Networks 12 (1999): 783～789
[117] Victor L Braillovsky, Ofir Barzily, Rabin Shahave. ON Global, Local, Mixed and Neighborhood Kernels for support Vector Machines. Pattern Recognition Letters, 20 (1999): 1183～1190
[118] Danny Boobaert. Improving the Generation of Linear Support Vector Machines: an Application to 3D Object Recognition with Cluttered Background. In: Proceedings SVM Workshop at 16th International Joint Conference on Artificial Intelligence (IJCAI99), Stockholm, Sweden, August 1999
[119] Guodong Guo, Stan Z. Li, Kap Luk Chan. Support Vector Machines for Face Recognition. Image and Vision Computing 19 (2001): 631～638
[120] 卢增祥，李衍达．交互支持向量学习及其应用．清华大学学报(自然科学版)，1999，39 (7)：93～97
[121] Harris Drucker, Behzad Shahrary, David C Gibbon. Support Vector Machines: Relevance feedback and Information Retrieval. Information processing and management, 38 (2002): 305～323
[122] Edgar Osuna, Robert Freund. An Improved Training Algorithms for Support Vector. In the Proc. Of IEEE NNST'97, Amclia Island, FL, 24 26 Sep. 1997
[123] Vapnik V. Estimation of Dependences Based on Empirical Data. Springer-Verlag, 1982
[124] Cortes C, Vapnik V. Support Vector Networks. Machine Learning, 1995, 20: 273～297
[125] Platt J C. Fast Training of SVMs Using Sequential Minimal Optimization, In: Scholkopf B, Burges C J, Smola A J eds., Advances in Kernel Methods-support Vector Learning Cambridge, MA: MIT Press, 1998: 185～208
[126] Osuna E, Freund R, Girosi F. "Improved Training Algorithm for Support Vector Machines" Proc. IEEE NNSP '97, (1997)
[127] Joachims T. Making Large-Scale SVM Learning Practical. In: Scholkopf B, Burges C J C, Smola A eds., Advances in Kernel Methods—Support Vector Learning, Cambridge, MA:MIT Press, 1998: 169～184
[128] http:/elib.cs.berkeley.edu/photos/
[129] Virginia E Ogle. Chabot: Retrieval from a Relational Database of Images. IEEE on computer, 1995 September: 40～48
[130] Rui Y, Huang T S, Mehrotra S et al. Relevance Feedback: A Power Tool for Interactive Content- Based Image Retrieval. IEEE Trans. Circuits and Systems for Video Technology. 1998, 8 (5): 644～655
[131] Theo Gevers, Arnold W M Smeulders. Color-Based Object Recognition. Pattern Recognition 32(1999): 453～464
[132] (日)玄光男，程润伟．遗传算法与工程设计．北京：科学出版社，2000
[133] M Pietikainen et al. Accurate Color Discrimination with Classification Based on Feature Distributions International Conference on Pattern Recognition. C, 1996, 833～838
[134] E Littmann, H Ritter. Adaptive Color Segmentation-a Comparison of Neural and Statistical Methods. IEEE Trans. Neural Network, 1997, 8 (1): 175～185
[135] 黄庆明等．基于色彩学习的彩色图像分割方法．计算机研究与发展，1995，32 (9)：60～64
[136] Nevatia. A Color Edge Detector and its Use in Scene Segmentation. IEEE Trans. System Man Cybernet. SMC-7 (11) (1997): 820～826
[137] I Andreadis, M A Browne, J A Swift. Image Pixel Classification by Chromaticity Analysis, Pattern Recognition Letters. 11 (1990): 51～58
[138] H D Cheng, X H Jiang. Color Image Segmentation: Advances and Prospects. Pattern Recognition, 34 (2001): 2259～2281
[139] Zhang Y J, Yao Y R. Comparison and Improvement of Color-Based Image Retrieval techniques. SPIE 1998, 3312: 371～382
[140] W S Kim, R H Park. Color Image Palette Construction Based on the HSI Color System for Minimizing the Reconstruction Error. IEEE International Conference on Image Processing, 1996: 1041～1044
[141] Smith A R. Color Gamut Transform Pairs. Computer Graphics, 1978, 12: 12～19
[142] P W M Tsang, W H Tsang. Edge Detection on Object Color. IEEE International Conference on Image Processing, 1996: 1049～1052
[143] K M Kim, C S Lee, Y H Ha. Color Image Quantization Using Weighted Distortion Measure of HVS Color Activity. IEEE International Conference on Pattern Recognition, 1996: 1035～1039
[144] 李国辉，柳伟，曹莉华．一种基于颜色特征的图象检索方法．中国图象图形学报，1999，4 (3)：248～255
[145] M S Kankanhalli, B M Mehtre, J K Wu. Cluster Based Color Matching for Image Retrieval. Pattern Recognition, 1996, 20 (4): 701～708
[146] M A Stricker, M Orengo. Similarity of Color Image. In: Proc of SPIE: Storage and Retrieval for Image and Viedo Databases Ⅲ, San Jose, CA, 1995, 2420: 381～392
[147] 柳伟，李国辉等．一种基于内容的图象检索方法的实现．中国图象图形学报，1998，3 (4)：304～308
[148] 刘德全．计算机辅助排样的理论、方法及其实现．[博士论文]大连：大连理工大学，1998
[149] W Hsu, T S Chua, H K Pung. An Integrated Color-Spatial Approach to Content-Based Image Retrieval. ACM Multimedia Conf., 1995: 305～313
[150] Kenneth R Castleman. Digital Image Processing. 北京：清华大学出版社, 1998
[151] 夏德深，傅德胜．现代图像处理技术与应用．南京：东南大学出版社，2001
[152] Y Ohta, T Kanade, T Sakai. Color Information for Region Segmentation. Comput. Graphics Image Process, 13 (1980): 222～241
[153] 卢汉清，孔维新等．基于内容的视频信号与图像库检索中的图像技术．自动化学报，2001，27 (1)：56～69
[154] Rosenfeld A. Algorithms for Image Vector Conversion. Computer Graphics, 1978, 12 (3): 135～139
[155] Persoon E, Fu K S. Shape Discrimination Using Fourier Descriptors. IEEE Trans. System, Man, Cybernetics, 1977, 7 (3): 170～179
[156] 周明，孙树栋．遗传算法原理及其应用．北京：国防工业出版社，1999
[157] Feng Q X, Francesco P. Theoretical Analysis of Evolutionary Algorithms with an Infinite Population Size in Continuous Space Part Ⅰ and Ⅱ: Basic Properties of Selection and Mutation. IEEE Trans. Neural Networks, 1994, 5 (1): 102～129
[158] 段玉倩．遗传算法及其改进．电力系统及自动化学报，1998，10 (1)：39～52
[159] 陈国龙．遗传算法在求解全局优化问题中的应用．福州大学学报．1999：27 (5)：14～18
[160] Rudolph G. Convergence Analysis of Canonical Genetic Algorithms. IEEE Trans on Neural Networks, 1994, 5 (1): 96～101
[161] 恽为民，席裕庚．遗传算法的全局收敛性和计算效率分析．控制理论与应用，1996，13 (4)：454～460
[162] A Tremeau, N Borel. A Region Growing and Merging Algorithm to Color Segmentation. Pattern Recognition, 1997, 30 (7): 1191～1203
[163] D N Chun, H S Yang. Robust Image Segmentation Using Genetic Algorithm with a Fuzzy Measure. Pattern Recognition 1996, 29 (7): 1195～1211
[164] Radcliffe, N And P Surry. Formal Memetic Algorithms. Evolutionary Computing, 1994
[165] Otsu N. A Threshold Selection Method from Gray-Level Histograms. IEEE Trans, 1979, SMC9 (1): 62～66
[166] J Gua, H Z Shua, C Toumoulinb, L M Luoa. A novel algorithm for fast computation of Zernike moments. Pattern Recognition, 35 (2002) 2905～2911
[167] Courant R, Hibert B. Methods of Mathematical Physics. Vol. 1. New York: Widely-interscience, 1953
[168] Barzilay O, Brailovsky, V L. On domain knowledge and feature selection using a support vector machine. Pattern Recognition Letters, 1999, 20:475～484
[160] 周学海，李光亚等．图像数据模型研究．中国科学技术大学学报，1997，27 (3)：298～303
[170] Chang S K. Image Information Systems: Where Do We Go from Here. IEEE Trans on Knowledge and Data Engineering, 1992, 4 (5): 431～441
[2] Volker Roth. Content-based Retrieval from Digital Video. Image and Vision Computing 17 (1999): 531-540
[3] W Niblack, R Barber. The QBIC Project: Querying Images by Content Using Color, Texture, and Shape. In Storage and Retrieval for Image and Video Databases, SPIE, 1993, Feb, Vol.1908: 173～187
[4] Myron Flickner, Harpreef Sawhney. Query by Image and Video Content: the QBIC System. IEEE Computer, 1995, September: 23～32
[5] http://vrw.excalib.com:8015/cst
[6] Cox I., Miller M. PicHunter: Bayesian Relevance Feedback for Image Retrieval. In: Proceedings of the International Conference on Pattern Recognition. Vienna Austria, 1996:361～369
[7] Ma W Y, Manjunath B S. Texture Features and Learning Similarity. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, June 1996:425～430
[8] Rohini Srihari, Zhangfei Zhang and Aibing Rao. Intelligent Indexing and Semantic Retrieval of Multimodal Documents. Information retrieval, 2000, 2 (2): 245～275
[9] Rui Y, Huang T S. Content-Based Image Retrieval with Relevance Feedback in MARS. In: Proceedings of the IEEE International Conference on Image Processing Santa Barbara, California, October 1997: 815～818
[10] Peng J, Bhanu B. Probabilistic Feature Relevance Learning for Content-Based Image Retrieval. Computer Vision and Image Understanding 75 (1/2), 1999: 150～164
[11] Jing Peng, Bir Bhanu. Independent Feature Analysis for Image Retrieval. Pattern Recognition Letters, 22 (2001): 63～74
[12] 章毓晋编著．图象工程(上册)：图象处理和分析．清华大学出版社，1999
[13] 贾云得编著．机器视觉．科学出版社，2000
[14] Swain M J, Ballard D H. Color Indexing. int. J. Computer Vision, 1991, 7 (1): 11～32
[15] Carlo Columbo, Alberto Del Bimbo. Color-Induced Image Representation and Retrieval. Pattern Recognition, 32 (1999): 1685～1695
[16] Tao Chen, Li-Hui Chen. Fast Mapping Algorithm for Histogram to Binary Set Conversion. Pattern Recognition Letters, 21 (2000): 899～906
[17] 刘忠伟，章毓晋．利用局部累加直方图进行彩色图象检索．中国图象图形学报，1998，3(7)：533～537
[18] Sawhney H S, Hafner J L. Efficient Color Histogram Indexing. IEEE International Conference on Image Processing, 1994, 2:66～70
[19] Stricker M, Swan M. The Capacity of Color Histogram Indexing. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1994: 704～708
[20] B M Mehtre, M S Kankanhalli. Color Matching for Image Retrieval. Pattern Recognition Letters, 16 (1995): 325～331
[21] S C Pie, C M Cheng. Extracting Color Feature and Dynamic Matching for Image DataBase Retrieval. IEEE Trans. Circuits Syst. Video Technol, 1999, 9 (3): 501～512
[22] L Cinque, G Ciocca, S Levialdi. Color-Based Image Retrieval Using Spatial-Chromatic Histograms. Image and Vision Computing, 19 (2001): 979～986
[23] M Stricker, A Dimai. Color Indexing With Weak Spatial Constraints. SPIE Proceedings. 1996, 2670: 29～40
[24] J R Smith, F S Chang. Tools and Techniques for Color Image Retrieval, Symposium on Electronic Imaging: Science and Technology-Storage and Retrieval for Image and Video Database IV, Vol. 2670, 1996: 426～237
[25] Nicu Sebe, Michael S Lew. Color-Based Retrieval. Pattern Recognition Letters, 22 (2001): 223～230
[26] Yu Zhong, Anil K. Jain. Object Localization Using Color, Texture and Shape. Pattern Recognition, 33(2000): 671～684
[27] Ji Yeun Kim. Color Indexing Using Chromatic Invariant. Pattern Recognition, 34 (2001): 1189～1197
[28] E Binaghi, I Gagliardi. Image Retrieval Using Fuzzy Evaluation of Color Similarity. Int. J. Pattern Recognition Artificial Intelligence, 1994, 8: 945～968
[29] Y Rubner, C Tomasi. A Metric for Distributions with Applications to Image Database. Proc. of the IEEE Int. Conf. on ComputerVision, 1998: 59～66
[30] Venkat N Gudivada. Content-Based Image Retrieval System. Computer, 1995, September, 18～22
[31] 金韬，任秀丽．图像检索中颜色特征的提取与匹配．计算机辅助设计与图形学报，2000，12 (6)：459～462
[32] Jia-Guu Leu. On Indexing the Periodicity of Image Textures. Image and Vision Computing, 19(2001): 987～1000
[33] Haralick R M, Shangmugam, Dinstein. Textural Feature for Image Classification. IEEE Trans on System, Man, Gybernetics, 1973, SMC-3 (6): 610～621
[34] 洪继光．灰度-梯度共生矩阵纹理分析方法．自动化学报．1984，10 (1)：22～25
[35] A Drimbarean, P F Whelan. Experiments in Colour Texture Analysis. Pattern Recognition Letters, 22 (2001): 1161～1167
[36] George Paschos. Fast Color Texture Recognition Using Chromaticity Moments. Pattern Recognition Letters, 21 (2000): 837～841
[37] Hao He, Yan Qiu Chen. Unsupervised Texture Segmentation Using Resonance Algorithm for Natural Scenes. Pattern Recognition Letters, 21 (2000): 741～757
[38] Jiebo Luo. Self-supervised Texture Segmentation Using Complementary Types of Features. Pattern Reconition, 34 (2001): 2071～2082
[39] Timo Ojala, Mattti Piekinen. Unsupervised Texture Segmentation Using Feature Distributions. Pattern Recognition, 32 (1999): 477～486
[40] http://deecom03.poliba.it/DrawSearch/Drawsearch.html
[41] Smith J R, Chang S F. Transform Feature for Texture Classification and Discrimination in Large Image Database. In: Proc of IEEE Int'l Conf on Image Processing. Austin, Texas, 1994
[42] 赵健，张文军．一种有效的基于小波帧变换包的纹理分类方法．计算机辅助设计与图形学学报，1997，9 (6)：520～526
[43] Wen-Shou Chou. Classifying Image Pixels into Shaped, Smooth, and Textured Points. Pattern Recognition, 32 (1999): 1697～1706
[44] Kalle Karu, Anil K Jain, Is There Any Texture in the Image. Pattern Recognition, 1996, 29 (9):
1437-1446
[45] Dmitry Chetverikov. Texture Analysis Using Feature-Based Pairwise Intersection Maps. Pattern recognition, 32 (1999): 487～502
[46] Damian G Jones, Paul T Jackway. Granolds: A novel Texture Representation. Pattern Recognition, 33 (2000): 1033～1045
[47] S Baheerathan, F Albregtsen. New Texture Features Based on the Complexity Curve. Pattern Recognition Letters, 32 (1999): 605～618
[48] David A Clausi, M Ed Jernigan. Designing Gabor Filters for Optimal Texture Separability. Pattern Recognition 33 (2000): 1835～1840
[49] M Pietikinen, T Ojala, Z Xu. Rotation-invariant Texture Classification using Feature Distributions. Pattern Recognition, 33 (2000): 43～52
[50] Richard E Frye, Robert S Ledley. Texture Discrimination Using Discrete Cosine Transformation Shift-insensitive(DCTSIS)] Descriptors. Pattern Recognition, 33(2000): 1585～1598
[51] 丁险峰，吴洪，张宏江，马颂德．形状匹配综述．自动化学报，2001，27 (5)：678～694
[52] Rajiv Mehrotra, James E. Gray. Similar-Shape Retrieval in Shape Data Management. IEEE Computer, September 1995:57～62
[53] Freeman H. On the Encoding of Arbitrary Geometric Configurations. IRE Trans, 1961, 10: 260～268
[54] Freeman H. Computer Processing of Line Drawing Images. ACM Computing Surveys, 1974, 6: 57～97
[55] Freeman H. Shape Description via the Use of Critical Points. Pattern Recognition, 1978, 10: 159～166
[56] Freeman H. Comparative Analysis of Line Drawing Modeling schemes. Computer Graphics Image Process, 1980, 12: 203～223
[57] Terry Caelli, David Reye. On the Classification of Image Regions by Color, Texture and Shape. Pattern Recognition, 1993, 26 (4): 461～470
[58] Hu M K. Pattern Recognition by Moment Invariant. In: Proc. IRE, 1961, 49: 1428
[59] Keping Chen. Efficient Parallel Algorithms for the Computation of Two-Dimensional Image Moments. Pattern Reconition, 1990, 23 (1/2): 109～119
[60] Miao Zhenjiang. Zernike Moment-based Image Shape Analysis and its Application. Pattern Recognition Letters, 21 (2000): 169～177
[61] CHO-HAK THE and Roland T. CHIN. On Image Analysis by the Methods of Moments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1988, 10 (4): 496～513
[62] Moment-Based Image-Normalization with High Noise-Tolerance. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1997, 19 (2): 136～139
[63] Alexander G Mamistvalov. N-Dimensional Moment Invariants and Conceptual Mathematical Theory of Recognition n-Dimensional Solids. IEEE Transaction on Pattern Analysis and Intelligence, 1998, 20 (8): 819～831
[64] Jain A K, Vailaya. Shape-Based Retrieval: A Case Study with Trademark Image Database. Pattern Recognition, 1998, 31 (9): 1369～1390
[65] Yang L, Algregtsen. A Fast Computation of Invariant Geometric Moments: A New Method Giving Correct Results. In: Proc of IEEE Int'l Conf on Image Processing. Austin Texas, 1994
[66] Kapur D, Lakshman Y N, Saxena T. Computing Invariant Using Elimination Methods, In: Proc of Int'1 Sysposium on Computer Vision. Florida, 1995: 97～102
[67] Ivar Balslev, Kasper Dring. Weighted Central Moments in Pattern Recognition. Pattern Recognition Letters, 21 (2000): 381～384
[68] Zhan C T, Roskies R Z. Fourier Descriptors for Plane Closed Curves. IEEE Trans. Computer, 1972, 21 (3): 269～281
[69] Rui Y, Alfred C, Huang T S. Modified Fourier Descriptor for Shape Representation, a Practical Approach. In: Proc. of First Int'1 Workshop on Image Database and Multimedia. Search, 1996
[70] Chen Guangyi, Tien D Bui. Invariant Fourier Wavelet Descriptor for Pattern Recognition. Pattern Recognition, 1999, 32 (7): 1083～1088
[71] Tieng Q M, Boles W W. Recognition of 2 D Object Contours Using the Wavelet Transform Zero- Crossing Representation. IEEE Trans. PAMI, 1997, 19 (8): 910～916
[72] 杨翔英，章毓晋．小波轮廓描述符及在图像查询中的应用．计算机学报，1999，22 (7)：752～757
[73] S Volmer. Tracing Images in Large Databases by Comparison of Wavelet Fingerprints. In Proceedings of the 2nd International Conference on Visual Information Systems, San Diego, 1997, 12: 163～172
[74] Ansari N, Delp E J. On the Distribution of a Deforming Triangle. Pattern Recognition, 1990, 23 (12): 1333～1341
[75] Young Susan S, Scott Peter D, Nasrabadi Nasser M. Object Recognition Using Multiplayer Hopfield Neural Network. IEEE Trans. Image Processing, 1997, 6 (3): 357～372
[76] Rajpal Navi, Chaudhury Stantanu, Banerjee Subhashis. Recognition of Partially Occluded Objects Using Neural Network Based Indexing. Pattern Recognition, 1999, 32:1737～1749
[77] http://attrasoft.com/abm34.html
[78] Anil K. Jain, Yu Zhong. Object Matching Using Deformable Templates. IEEE Transactions on Pattern Analysi and Machine Intelligence, 1996, 18 (3): 267～277
[79] Alberto Del Bimbo, Pietro. Visual Image Retrieval by Elastic Matching of User Sketches. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1997, 19 (2): 121～131
[80] P Pala, S Santini. Image Retrieval by Shape and Texture. Pattern Recognition, 32 (1999): 517～527
[81] J M Fuertes, M Lucena. A Scheme of Color Image Retrieval from Databases. Pattern Recognition Letters, 22 (2001): 323～337
[82] 杨新，郭丰俊等．一类图象匹配方程的有限差分解法．上海交通大学学报，1998，32 (9)：53～56
[83] 周杰，卢春雨等．人脸自动识别方法综述．电子学报，2000，28 (4)：102～106
[84] http://www.ee.surrey.ac.uk/Research/VSSP/imagedb/squid.html
[85] Anil K Jain, Aditya Vailaya. Image Retrieval Using Color and Shape. Pattern Recognition, 1996, 29 (8): 1233～1244
[86] Ravikanth Malladi, James A Sthian. A Unified Approach to Noise Removal, Image Enhancement, and Shape Recovery. IEEE Transaction on Image Processing, 1996, 5 (11): 1554～1568
[87] 邬浩，潘云鹤等．基于对象形状的图象检索查询技术．软件学报，1998，9 (5)：343～349
[88] Yung-Kuan Chan, Chin-Chen Chang. Image Matching Using Run-Length Feature. Pattern Recognition Letter, 22 (2001): 447～455
[89] Hirobumi Nishida. Structural Shape Indexing with Feature Generation Models. Computer Vision and Image Understanding, 1999, 73 (1): 121～136
[90] Pentland, Picard R W, Sclaroff S. Photo Book: Tools for Content-Based Manipulation of Image
Database. International Journal of Computer Vision, 1996, 18 (3)
[91] J Serra. Image Analysis and Mathematical Morphology. Theoretical Advances. Academic Press, London, 1988, 2
[92] Haralick R M, Shapiro L G. Computer and Robot Vision. 1992, Vol.1, Addison-Wesley
[93] Vincent L. Morphological Greyscale Reconstruction in Image Analysis: Applications and Efficient Algorithms. IEEE Trans. On Image Processing, 1993, 2 (2): 176～201
[94] 崔屹．图像处理与分析—数学形态学方法及应用．北京：科学出版社，2000
[95] Cirmmons T, Brown W. Image Algebra and Automatic Shape Processing. IEEE Trans. Aerospace and Electronic Systems, January, 1985, 221
[96] Bhattacharya P, Zhu W. Shape Recognition Method Using Morphological Hit-or Miss Transform. Optical Engineering, 1995, 34 (6): 1728～1725
[97] Computing Geometric Moments Using Morphological Erosions. Pattern recognition 34 (2001): 271～276
[98] A Hill, C J Taylor. Model-Based Image Interpretation Using Genetic Algorithms. Image and Vision Computing. 1992, 10 (5): 295～300
[99] Michalalewicz Z. Genetic Algorithm+Data Structure=Evolution Programs, 2nd ed., Springer-Verlag, New York, 1994
[100] Grefenstette J J. Optimization of Control Parameters for Genetic Algorithms. IEEE Trans. On Syst., Man & Cybern, 1986, 16 (1): 122～128
[101] Goldberg D E. Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA: Addition-Wesley, 1989
[102] 石琳珂，孙铭心等．地球物理遗传反演方法．北京：地震出版社，2000
[103] 徐宗本，高勇．遗传算法过早收敛现象的特征分析及其预防．中国科学，1996，26 (4)：564～375
[104] Davis L. Handbook of Genetic Algorithms. New York: Van Nostrand Reinhold, 1991
[105] Srinivas M, Patnaik L M. Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms. IEEE Trans. Syst Man and Cyber, 1994, 24 (4): 656～667
[106] 宋爱国，陆佶人．一种基于排序操作的优化算子自适应遗传算法．电子学报，1999，27 (1)：85～88
[107] Shu-Chien Huang, Yung-Nien Sun. Polygonal Approximation Using Genetic Algorithms. Pattern Recognition, 32 (1999): 1409～1420
[108] Shiu Yin Yuen, Chi Ho Ma. Genetic Algorithm with Competitive Image Labeling and Least Square. Pattern Recognition, 33 (2000): 1949～1966
[109] 张元亮，郑南宁等．基于遗传算法的混合分形编码．自动化学报，1999，25 (1)：142～144
[110] 陈宁江，李介谷等．遗传算法在立体图象匹配中的应用．上海交通大学学报，1998，32 (9)：49～52
[111] Motohide Yoshimura, Shunichiro Oe. Evolutionary Segmentation of Texture Image Using Genetic Algorithms towards Automatic Design of Optimum Number of Segmentation Areas. Pattern Recognition, 32 (1999): 2041～2054
[112] Vapnik V, Lerner A. Pattern Recognition Using Generalized Portrait. Automation and Remote Control. 1963, 24 (6): 774～780
[113] Pierre M L, Drezet, Robert F Harrison. A New Method for Sparsity Control in Support Vector Classification and Regression. Pattern Recognition 34 (2001): 111～125
[114] David M J Tax, Robert P W Duin. Support Vector Domain Description. Pattern
Recognition Letters, 20 (1999): 1191～1199
[115] Joao Pedro Pedroso. Support Vector Machines with Different Norms: Motivation, Formulations and Results. Patter Recognition Letters, 22 (2001): 1263～1272
[116] S Amari, S Wu. Improving Support Vector Machine Classifiers by Modifying Kernel Functions. Neural Networks 12 (1999): 783～789
[117] Victor L Braillovsky, Ofir Barzily, Rabin Shahave. ON Global, Local, Mixed and Neighborhood Kernels for support Vector Machines. Pattern Recognition Letters, 20 (1999): 1183～1190
[118] Danny Boobaert. Improving the Generation of Linear Support Vector Machines: an Application to 3D Object Recognition with Cluttered Background. In: Proceedings SVM Workshop at 16th International Joint Conference on Artificial Intelligence (IJCAI99), Stockholm, Sweden, August 1999
[119] Guodong Guo, Stan Z. Li, Kap Luk Chan. Support Vector Machines for Face Recognition. Image and Vision Computing 19 (2001): 631～638
[120] 卢增祥，李衍达．交互支持向量学习及其应用．清华大学学报(自然科学版)，1999，39 (7)：93～97
[121] Harris Drucker, Behzad Shahrary, David C Gibbon. Support Vector Machines: Relevance feedback and Information Retrieval. Information processing and management, 38 (2002): 305～323
[122] Edgar Osuna, Robert Freund. An Improved Training Algorithms for Support Vector. In the Proc. Of IEEE NNST'97, Amclia Island, FL, 24 26 Sep. 1997
[123] Vapnik V. Estimation of Dependences Based on Empirical Data. Springer-Verlag, 1982
[124] Cortes C, Vapnik V. Support Vector Networks. Machine Learning, 1995, 20: 273～297
[125] Platt J C. Fast Training of SVMs Using Sequential Minimal Optimization, In: Scholkopf B, Burges C J, Smola A J eds., Advances in Kernel Methods-support Vector Learning Cambridge, MA: MIT Press, 1998: 185～208
[126] Osuna E, Freund R, Girosi F. "Improved Training Algorithm for Support Vector Machines" Proc. IEEE NNSP '97, (1997)
[127] Joachims T. Making Large-Scale SVM Learning Practical. In: Scholkopf B, Burges C J C, Smola A eds., Advances in Kernel Methods—Support Vector Learning, Cambridge, MA:MIT Press, 1998: 169～184
[128] http:/elib.cs.berkeley.edu/photos/
[129] Virginia E Ogle. Chabot: Retrieval from a Relational Database of Images. IEEE on computer, 1995 September: 40～48
[130] Rui Y, Huang T S, Mehrotra S et al. Relevance Feedback: A Power Tool for Interactive Content- Based Image Retrieval. IEEE Trans. Circuits and Systems for Video Technology. 1998, 8 (5): 644～655
[131] Theo Gevers, Arnold W M Smeulders. Color-Based Object Recognition. Pattern Recognition 32(1999): 453～464
[132] (日)玄光男，程润伟．遗传算法与工程设计．北京：科学出版社，2000
[133] M Pietikainen et al. Accurate Color Discrimination with Classification Based on Feature Distributions International Conference on Pattern Recognition. C, 1996, 833～838
[134] E Littmann, H Ritter. Adaptive Color Segmentation-a Comparison of Neural and Statistical Methods. IEEE Trans. Neural Network, 1997, 8 (1): 175～185
[135] 黄庆明等．基于色彩学习的彩色图像分割方法．计算机研究与发展，1995，32 (9)：60～64
[136] Nevatia. A Color Edge Detector and its Use in Scene Segmentation. IEEE Trans. System Man Cybernet. SMC-7 (11) (1997): 820～826
[137] I Andreadis, M A Browne, J A Swift. Image Pixel Classification by Chromaticity Analysis, Pattern Recognition Letters. 11 (1990): 51～58
[138] H D Cheng, X H Jiang. Color Image Segmentation: Advances and Prospects. Pattern Recognition, 34 (2001): 2259～2281
[139] Zhang Y J, Yao Y R. Comparison and Improvement of Color-Based Image Retrieval techniques. SPIE 1998, 3312: 371～382
[140] W S Kim, R H Park. Color Image Palette Construction Based on the HSI Color System for Minimizing the Reconstruction Error. IEEE International Conference on Image Processing, 1996: 1041～1044
[141] Smith A R. Color Gamut Transform Pairs. Computer Graphics, 1978, 12: 12～19
[142] P W M Tsang, W H Tsang. Edge Detection on Object Color. IEEE International Conference on Image Processing, 1996: 1049～1052
[143] K M Kim, C S Lee, Y H Ha. Color Image Quantization Using Weighted Distortion Measure of HVS Color Activity. IEEE International Conference on Pattern Recognition, 1996: 1035～1039
[144] 李国辉，柳伟，曹莉华．一种基于颜色特征的图象检索方法．中国图象图形学报，1999，4 (3)：248～255
[145] M S Kankanhalli, B M Mehtre, J K Wu. Cluster Based Color Matching for Image Retrieval. Pattern Recognition, 1996, 20 (4): 701～708
[146] M A Stricker, M Orengo. Similarity of Color Image. In: Proc of SPIE: Storage and Retrieval for Image and Viedo Databases Ⅲ, San Jose, CA, 1995, 2420: 381～392
[147] 柳伟，李国辉等．一种基于内容的图象检索方法的实现．中国图象图形学报，1998，3 (4)：304～308
[148] 刘德全．计算机辅助排样的理论、方法及其实现．[博士论文]大连：大连理工大学，1998
[149] W Hsu, T S Chua, H K Pung. An Integrated Color-Spatial Approach to Content-Based Image Retrieval. ACM Multimedia Conf., 1995: 305～313
[150] Kenneth R Castleman. Digital Image Processing. 北京：清华大学出版社, 1998
[151] 夏德深，傅德胜．现代图像处理技术与应用．南京：东南大学出版社，2001
[152] Y Ohta, T Kanade, T Sakai. Color Information for Region Segmentation. Comput. Graphics Image Process, 13 (1980): 222～241
[153] 卢汉清，孔维新等．基于内容的视频信号与图像库检索中的图像技术．自动化学报，2001，27 (1)：56～69
[154] Rosenfeld A. Algorithms for Image Vector Conversion. Computer Graphics, 1978, 12 (3): 135～139
[155] Persoon E, Fu K S. Shape Discrimination Using Fourier Descriptors. IEEE Trans. System, Man, Cybernetics, 1977, 7 (3): 170～179
[156] 周明，孙树栋．遗传算法原理及其应用．北京：国防工业出版社，1999
[157] Feng Q X, Francesco P. Theoretical Analysis of Evolutionary Algorithms with an Infinite Population Size in Continuous Space Part Ⅰ and Ⅱ: Basic Properties of Selection and Mutation. IEEE Trans. Neural Networks, 1994, 5 (1): 102～129
[158] 段玉倩．遗传算法及其改进．电力系统及自动化学报，1998，10 (1)：39～52
[159] 陈国龙．遗传算法在求解全局优化问题中的应用．福州大学学报．1999：27 (5)：14～18
[160] Rudolph G. Convergence Analysis of Canonical Genetic Algorithms. IEEE Trans on Neural Networks, 1994, 5 (1): 96～101
[161] 恽为民，席裕庚．遗传算法的全局收敛性和计算效率分析．控制理论与应用，1996，13 (4)：454～460
[162] A Tremeau, N Borel. A Region Growing and Merging Algorithm to Color Segmentation. Pattern Recognition, 1997, 30 (7): 1191～1203
[163] D N Chun, H S Yang. Robust Image Segmentation Using Genetic Algorithm with a Fuzzy Measure. Pattern Recognition 1996, 29 (7): 1195～1211
[164] Radcliffe, N And P Surry. Formal Memetic Algorithms. Evolutionary Computing, 1994
[165] Otsu N. A Threshold Selection Method from Gray-Level Histograms. IEEE Trans, 1979, SMC9 (1): 62～66
[166] J Gua, H Z Shua, C Toumoulinb, L M Luoa. A novel algorithm for fast computation of Zernike moments. Pattern Recognition, 35 (2002) 2905～2911
[167] Courant R, Hibert B. Methods of Mathematical Physics. Vol. 1. New York: Widely-interscience, 1953
[168] Barzilay O, Brailovsky, V L. On domain knowledge and feature selection using a support vector machine. Pattern Recognition Letters, 1999, 20:475～484
[160] 周学海，李光亚等．图像数据模型研究．中国科学技术大学学报，1997，27 (3)：298～303
[170] Chang S K. Image Information Systems: Where Do We Go from Here. IEEE Trans on Knowledge and Data Engineering, 1992, 4 (5): 431～441