基于区域模糊特征的图像检索研究和实现
|
摘要
随着计算机技术、多媒体技术的迅速发展以及Internet 的不断扩大,图像信息变得越来越丰富,如何快速地找到需要的图像成为亟待解决的问题。基于内容的图像检索技术旨在搜索出满足人们主观要求的图像,因此得到了广泛研究。
模糊集理论能够促进基于内容的图像检索技术发展,使图像检索技术脱离精确的计算,更符合人类的模糊思维。论文着眼于这一点,讨论了融合模糊集理论的图像检索技术,分析比较了模糊聚类算法FCM 和人工免疫网络aiNet 聚类方法各自的特性,最后提出了基于区域模糊特征的图像检索的改进方法,使原有方法在速度和命中准确率上都得到提高,实现的系统也证实了这一点。
论文主要作了以下工作:
¨¨系统地总结了基于内容图像检索的几方面关键技术,包括:低层视觉特征的提取算法、图像特征数据库的索引机制、相似性度量方法、图像检索查询方式、相关性反馈技术和图像检索算法性能的评价策略。
¨¨讨论了这几种模糊特征提取技术:简单的颜色模糊直方图、基于颜色隶属模型的直方图、颜色和纹理综合模糊直方图方法、模糊形状表达技术。
¨¨分析比较了模糊C 均值聚类算法FCM 和人工免疫网络aiNet 聚类算法的特性,通过它们对相对集中数据、稀疏分散数据、环状和螺旋分布数据聚类来进行比较,得到当遇到潜在的类或簇背离超球面结构时,FCM 算法表现不佳,而aiNet 可以很好的发现数据的内在特征,减少数据中的冗余、描述数据结构和聚类形状,表现良好的适应性。
¨¨提出并且实现了基于区域模糊特征的图像检索的改进方法。有三点改进:采用量化再累计的方法来减少参与聚类运算的数据,而不影响聚类结果;使用边界矩来表征区域的形状特征;使用邻接表表征区域空间分布特征。实验表明,改进方法在速度和命中准确率上都得到提高。
With the rapid development of technologies of computer and multimedia and the wide-spread use of Internet, there is more and more of image information. How to rapidly find out images needed is starving for resolve. Content-based Image Retrieval (CBIR) is aimed at searching out images which satisfy user's needs, so is widely researched.
Fuzzy set theory advances development of CBIR, extends CBIR from precision of calculating and caters for human's fuzzy thoughts. Emphasizing on this character, the thesis discusses image retrieval techniques which are syncretized Fuzzy set, analyzes and compares the characters of Fuzzy c-Means clustering and artificial immune network aiNet clustering, finally presents an improved region-based fuzzy feature to content-based image retrieval better in speed and hit rate than the original method as the realized system proves.
The main works are as follows.
¨¨To summarize key techniques of modules of CBIR system such as feature extraction, indexing scheme, similarity measure, query specification, relevance feedback and performance evaluation.
¨¨To discuss some fuzzy techniques of feature extraction including crude fuzzy histograms, fuzzy paradigm-based histograms, combined fuzzy histograms and fuzzy geometrical features.
¨¨To analyze and compare the characters of Fuzzy c-Means clustering and artificial immune network aiNet clustering, conclude that Fuzzy c-Means clustering does not work well if the underlying classes or clusters deviate strongly from hyperspherical structures, aiNet is capable of reducing redundancy, describing data structure, including the shape of clusters by experiments on relatively gathering data set, scattering data set, annular data set and corkscrew data set.
¨¨To present and realize an improved region-based fuzzy feature to content-based image retrieval. On the basis of original method, three improvements are presented, they are data reduction but no influence to results by quantization and aggregation to Fuzzy c-Means, describing regions by shape based moments and adding feature of spatial information by neighboring table. Experiment results show that the new is better in speed and hit rate.
模糊集理论能够促进基于内容的图像检索技术发展,使图像检索技术脱离精确的计算,更符合人类的模糊思维。论文着眼于这一点,讨论了融合模糊集理论的图像检索技术,分析比较了模糊聚类算法FCM 和人工免疫网络aiNet 聚类方法各自的特性,最后提出了基于区域模糊特征的图像检索的改进方法,使原有方法在速度和命中准确率上都得到提高,实现的系统也证实了这一点。
论文主要作了以下工作:
¨¨系统地总结了基于内容图像检索的几方面关键技术,包括:低层视觉特征的提取算法、图像特征数据库的索引机制、相似性度量方法、图像检索查询方式、相关性反馈技术和图像检索算法性能的评价策略。
¨¨讨论了这几种模糊特征提取技术:简单的颜色模糊直方图、基于颜色隶属模型的直方图、颜色和纹理综合模糊直方图方法、模糊形状表达技术。
¨¨分析比较了模糊C 均值聚类算法FCM 和人工免疫网络aiNet 聚类算法的特性,通过它们对相对集中数据、稀疏分散数据、环状和螺旋分布数据聚类来进行比较,得到当遇到潜在的类或簇背离超球面结构时,FCM 算法表现不佳,而aiNet 可以很好的发现数据的内在特征,减少数据中的冗余、描述数据结构和聚类形状,表现良好的适应性。
¨¨提出并且实现了基于区域模糊特征的图像检索的改进方法。有三点改进:采用量化再累计的方法来减少参与聚类运算的数据,而不影响聚类结果;使用边界矩来表征区域的形状特征;使用邻接表表征区域空间分布特征。实验表明,改进方法在速度和命中准确率上都得到提高。
With the rapid development of technologies of computer and multimedia and the wide-spread use of Internet, there is more and more of image information. How to rapidly find out images needed is starving for resolve. Content-based Image Retrieval (CBIR) is aimed at searching out images which satisfy user's needs, so is widely researched.
Fuzzy set theory advances development of CBIR, extends CBIR from precision of calculating and caters for human's fuzzy thoughts. Emphasizing on this character, the thesis discusses image retrieval techniques which are syncretized Fuzzy set, analyzes and compares the characters of Fuzzy c-Means clustering and artificial immune network aiNet clustering, finally presents an improved region-based fuzzy feature to content-based image retrieval better in speed and hit rate than the original method as the realized system proves.
The main works are as follows.
¨¨To summarize key techniques of modules of CBIR system such as feature extraction, indexing scheme, similarity measure, query specification, relevance feedback and performance evaluation.
¨¨To discuss some fuzzy techniques of feature extraction including crude fuzzy histograms, fuzzy paradigm-based histograms, combined fuzzy histograms and fuzzy geometrical features.
¨¨To analyze and compare the characters of Fuzzy c-Means clustering and artificial immune network aiNet clustering, conclude that Fuzzy c-Means clustering does not work well if the underlying classes or clusters deviate strongly from hyperspherical structures, aiNet is capable of reducing redundancy, describing data structure, including the shape of clusters by experiments on relatively gathering data set, scattering data set, annular data set and corkscrew data set.
¨¨To present and realize an improved region-based fuzzy feature to content-based image retrieval. On the basis of original method, three improvements are presented, they are data reduction but no influence to results by quantization and aggregation to Fuzzy c-Means, describing regions by shape based moments and adding feature of spatial information by neighboring table. Experiment results show that the new is better in speed and hit rate.
引文
[1] Moores C N. Data coding applied to mechanical organization of knowledge. Am.Doc, 1951, 2:20-32.
[2] Chang N S, Fu K S. Query-by-pictorial example. IEEE Software Eng, 1980, 6(6):519-524.
[3] Kato T. Database architecture for content-based image retrieval. SPIE, 1992, 1662:112-113
[4] 周向东.图像数据库检索中的关键技术:[博士学位论文].上海:复旦大学.2003.
[5] 林建成.一种图像检索框架的研究及其原型系统的实现:[硕士学位论文].福州:福州大学,2004.
[6] 耿苑.结合低层特征和高层语义的图像检索系统:[硕士学位论文].西安:西北工业大学,2004.
[7] S. Kulkarni. Interpretation of Fuzzy Logic for Texture Queries in CBIR. Vision, Video, and Graphics,2003.
[8] 曹奎,冯玉才,曹忠升等.彩色图象检索中的模糊直方图技术.小型微型计算机系统,2001,22(7):833-836.
[9] C.Vertan, N.Noujeaa. Embedding fuzzy logic in content based image retrieval. Proc.19th Int’l Meeting North Am. Fuzzy Information Processing Soc. 2000, 85-89.
[10] 何恒,余英林.一种用于图象检索的综合模糊直方图方法.中国图象图形学报, 2001,6(A)(7):694-698.
[11] 梁艳梅, 翟宏深等. 基于模糊相关的彩色图像检索. 中国科学,2003, 33(7):934-938.
[12] Minakshi Banerjee, Malay K.Kundu. Content based image retrieval with fuzzy geometrical features. The IEEE International Conference on Fuzzy Systems, 2003, 932-937.
[13] Y Li, J ming ,et.al.The fuzzy similarity measures for content-based image retrieval. Proceedings of the Second International conference on machine learning and cybernetecs, Xi`an, 2003, 2-5.
[14] 陈纯.计算机图像处理技术与算法.第一版.北京:清华大学出版社,2003, 184-186.
[15] E.Mathias. Comparing the influence of color spaces and metrics in content-based image retrieval. Proceedings of International Sysposium on Computer Graphics, Image Processing and Vision, 1998, 371-378.
[16] B.M. Mehtre, M.S. Kankanhalli, A.D. Narasimhalu. Color matching for image retrieval. Pattern Recognition Letters, 1995, 16: 325-331.
[17] M. J. Swain, S. H. Ballard. Color Indexing. Int. Journal of Computer Vision, 1991,7 (1):11-32.
[18] Kankanhalli, M.S. Mehtre, B.M. Wu. Cluster-based color matching for image retrieval. Pattern Recognition, 1996, 29(4): 701-708.
[19] M.Stricker , M.Orengo. Similarity of Color Images. SPIE Storage and Retrieval for Image and Video Databases Ⅲ,1995.
[20] R.C.Gonzalez. Digital image processing. 2002,北京:电子工业出版社.
[21] G. Pass, R.Zabith, Histogram refinement for content-based image retrieval. IEEE Workshop on Applications of Computer Vision, 1996, 96-102.
[22] J. Huang, et al., Image indexing using color correlogram.IEEE Int. Conf. on Computer Vision and Pattern Recognition, 1997, 762-768.
[23] J. Huang, et al. spatial color indexing and applications. International Journal of Computer Vision, 1999, 19:345-360.
[24] M.S. Kankanhalli, B.M. Mehtre, H.Y. Huang. Color and spatial feature for content-based image retrieval. Pattern Recognition Letters, 1999, 20: 109-118.
[25] Furht B, et al.Video and image processing in multimedia systems, Boston: Kluwer Academic Publishers, 1995, 226-270.
[26] D.A. Adjeroh, M.C. Lee. On Ratio-Based Color Indexing. IEEE Trans. On Image Processing, 2001,10(1):36-48.
[27] B.V. Funt,G..D. Finlayson. Color constant color indexing. IEEE Trans. On PAMI, 1995, 17: 522-529.
[28] T.Gevers, A.W.M.Smeulders. Pictoseek: Combining color and shape invariant features for image retrieval IEEE Trans. on image processing, 2000 9(1):102-119.
[29] 洪安详.基于内容的图像检索若干论题研究:[博士学位论文].杭州:浙江大学2003,12-13.
[30] H. Tamura, S. Mori, T. Yamawaki Texture features corresponding to visual perception, IEEE Trans. On Systems, Man, and Cybernetics, 1978,8( 6).
[31] 李建平.小波分析与信号处理—理论、应用及软件实现. 重庆:重庆出版.2001.
[32] Stephane Mallat. A Wavelet Tour.of Signal Processing.杨力华,戴道清等.北京:机械工业出版社.2002,182-186.
[33] 任礼斌.小波分析在基于内容的图像检索中的应用研究:[硕士学位论文].沈阳:沈阳工业学院,2003.
[34] S. G. Mallat, A theory for multiresolution signal representation: the wavelet decomposition, IEEE Trans. On Pattern Analysis and Machine Intelligence, 1989, 11 ( 7):674-693.
[35] B. S. Manjunath, W. Y. Ma. Texture features for browsing and retrieval of image data.IEEE Trans. on Pattern Analysis and Machine Intelligence, 1996, 18(8):837-842.
[36] 冯象初,甘小冰,宋国乡.数值泛函与小波理论.西安:西安电子科技大学出版社,2002.
[37] 章毓晋.基于内容的视觉信息检索.北京:科学出版社,2003,104-105.
[38] 孙即祥.模式识别中的特征提取与计算机视觉不变量。北京:国防工业出版社,2001.
[39] T. Meiers, T.Ellerbrock, T. Sikora, Image browsing with PCA-assisted user-interaction.Content-Based Access of Image and Video Libraries,IEEE Workshop on , 2001,102-108.
[40] 韩军伟.基于内容的图象检索技术研究:[博士学位论文].西安:西北工业大学,2003.
[41] 王耀南.计算智能信息处理技术及其应用.长沙:湖南大学出版社,1999, 24-29.
[42] C.Vertan, V.Buzuloiu. Using fuzzy histograms and distances for color image retrieval. In Proc.of CIR’2000, Brighton,United Kingdom, 2000,4-5 .
[43] C.P.Papps, N.I.Karacapilidis. A comparative assessment of measures of similarity of fuzzy values. Fuzzy sets and systems (56), 1993,171-174.
[44] I.Bloch. On fuzzy distances and their use in image processing under imprecision. Pattern Recognition (32),1999 1873-1895.
[45] S-M Chen,M-S Yeh,P-Y Hsiao.A comparison of similarity measures of fuzzy values. Fuzzy sets and systems (72), 1995,79-89.
[46] R.C.Jain,S.Nmurthy, L.Tran.Similarity measures for image database. In proc.Of IEEE Conference on Fuzzy Systems, 1995, 3:1247-1254.
[47] W-J Wang. New similarity measures on fuzzy sets and on elements. Fuzzy Sets and Systems,1997,85:305-309.
[48] M.Granzow, D.Berrar, W.Dubitzky et al. Tumor Classification by Gene Expression Profiling: Comparison and Validation of Five Clustering Methods.ACM SIGBIO Newsletter, 2001,21(1).
[49] J.C.Bezdek. Pattern Recognition with Objective Function Algorithms.New York: Plenum, 1981.43-93.
[50] De Castro L.N.Artificial immune systems as a Novel Soft Computing Paradigm.Soft Computing Journal, 2003, 7(7).
[51] Jerne.N.K.Towards a Network Theory of the Immune Systems.Annual Immunology (1nst, Pasteur) 125-C, 73 –389.
[52] De Castro L.N., Von Zuben F.J.aiNet: An artificial immune network for data analysis. H.A. Abbas, R.A. Sarker, Ch.S. Newton, eds. Data Mining: A Heuristic Approach. USA: Idea Group Publishing, 2001, 231-259
[53] 莫宏伟.人工免疫系统原理与应用.哈尔滨:哈尔滨工业大学出版社.2002.
[54] C.Carson M.Thomas, S.Belongie, J.M. Hellerstein and J. Malik. Blobworld: a system for region-based image indexing and retrieval. Proc. Visual Information Systems, June 1999, 509-516.
[55] J.Z.Wang, J.Li and G.Wiederhold. SIMPLIcity: semantics-sensitive integrated matching for picture libraries. IEEE Trans. on PAMI, 2001,23 (9):1-17.
[56] J. Li, J.Z. Wang, G.Wiederhold. IRM: integrated region matching for image retrieval. Proceeding of ACM Multimedia, 2000.
[57] Y.X.Chen, J.Z.Wang. A Region-based fuzzy feature matching approach to content-based image retrieval. IEEE transactions on pattern analysis and machine intelligence,2002, 24(9),1252-1267.
[58] 梅丽霞, 田玉敏. 基于区域的图像数据库检索方法. 计算机工程. 2003,29(5):127-129.
[59] 黄元元,郭丽,杨静宇. 基于目标区域颜色与纹理特征的图像检索. 南京理工大学学报, 2003,27(3):286-289.
[60] MacQueen J. Some methods for classification and analysis of multivariate observations.Proc.5 Th Berkeley Symp. Math. Statist, Prob[C] 1967, 1:281 -297 .
[61] Huang Z. Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Mining and Knowledge Discovery,1998, 2: 283-304.
[62] A.Gersho. asymptotically optimum block quantization. IEEE Trans Information Theory,1979,25(4):373-380.
[63] 林开颜,徐立鸿,吴军辉. 快速模糊C 均值聚类彩色图像分割方法.中国图象图形学报,2004,9(2).
[64] Chen C C. Improved moment invariant for shape discrimination. Pattern recognition, 1993,26:683~686.
[65] Benny Johansen,Matthew Reynolds 等著,张哲峰译. Windows 应用高级编程—c#编程篇.北京:清华大学出版社,2002.
[2] Chang N S, Fu K S. Query-by-pictorial example. IEEE Software Eng, 1980, 6(6):519-524.
[3] Kato T. Database architecture for content-based image retrieval. SPIE, 1992, 1662:112-113
[4] 周向东.图像数据库检索中的关键技术:[博士学位论文].上海:复旦大学.2003.
[5] 林建成.一种图像检索框架的研究及其原型系统的实现:[硕士学位论文].福州:福州大学,2004.
[6] 耿苑.结合低层特征和高层语义的图像检索系统:[硕士学位论文].西安:西北工业大学,2004.
[7] S. Kulkarni. Interpretation of Fuzzy Logic for Texture Queries in CBIR. Vision, Video, and Graphics,2003.
[8] 曹奎,冯玉才,曹忠升等.彩色图象检索中的模糊直方图技术.小型微型计算机系统,2001,22(7):833-836.
[9] C.Vertan, N.Noujeaa. Embedding fuzzy logic in content based image retrieval. Proc.19th Int’l Meeting North Am. Fuzzy Information Processing Soc. 2000, 85-89.
[10] 何恒,余英林.一种用于图象检索的综合模糊直方图方法.中国图象图形学报, 2001,6(A)(7):694-698.
[11] 梁艳梅, 翟宏深等. 基于模糊相关的彩色图像检索. 中国科学,2003, 33(7):934-938.
[12] Minakshi Banerjee, Malay K.Kundu. Content based image retrieval with fuzzy geometrical features. The IEEE International Conference on Fuzzy Systems, 2003, 932-937.
[13] Y Li, J ming ,et.al.The fuzzy similarity measures for content-based image retrieval. Proceedings of the Second International conference on machine learning and cybernetecs, Xi`an, 2003, 2-5.
[14] 陈纯.计算机图像处理技术与算法.第一版.北京:清华大学出版社,2003, 184-186.
[15] E.Mathias. Comparing the influence of color spaces and metrics in content-based image retrieval. Proceedings of International Sysposium on Computer Graphics, Image Processing and Vision, 1998, 371-378.
[16] B.M. Mehtre, M.S. Kankanhalli, A.D. Narasimhalu. Color matching for image retrieval. Pattern Recognition Letters, 1995, 16: 325-331.
[17] M. J. Swain, S. H. Ballard. Color Indexing. Int. Journal of Computer Vision, 1991,7 (1):11-32.
[18] Kankanhalli, M.S. Mehtre, B.M. Wu. Cluster-based color matching for image retrieval. Pattern Recognition, 1996, 29(4): 701-708.
[19] M.Stricker , M.Orengo. Similarity of Color Images. SPIE Storage and Retrieval for Image and Video Databases Ⅲ,1995.
[20] R.C.Gonzalez. Digital image processing. 2002,北京:电子工业出版社.
[21] G. Pass, R.Zabith, Histogram refinement for content-based image retrieval. IEEE Workshop on Applications of Computer Vision, 1996, 96-102.
[22] J. Huang, et al., Image indexing using color correlogram.IEEE Int. Conf. on Computer Vision and Pattern Recognition, 1997, 762-768.
[23] J. Huang, et al. spatial color indexing and applications. International Journal of Computer Vision, 1999, 19:345-360.
[24] M.S. Kankanhalli, B.M. Mehtre, H.Y. Huang. Color and spatial feature for content-based image retrieval. Pattern Recognition Letters, 1999, 20: 109-118.
[25] Furht B, et al.Video and image processing in multimedia systems, Boston: Kluwer Academic Publishers, 1995, 226-270.
[26] D.A. Adjeroh, M.C. Lee. On Ratio-Based Color Indexing. IEEE Trans. On Image Processing, 2001,10(1):36-48.
[27] B.V. Funt,G..D. Finlayson. Color constant color indexing. IEEE Trans. On PAMI, 1995, 17: 522-529.
[28] T.Gevers, A.W.M.Smeulders. Pictoseek: Combining color and shape invariant features for image retrieval IEEE Trans. on image processing, 2000 9(1):102-119.
[29] 洪安详.基于内容的图像检索若干论题研究:[博士学位论文].杭州:浙江大学2003,12-13.
[30] H. Tamura, S. Mori, T. Yamawaki Texture features corresponding to visual perception, IEEE Trans. On Systems, Man, and Cybernetics, 1978,8( 6).
[31] 李建平.小波分析与信号处理—理论、应用及软件实现. 重庆:重庆出版.2001.
[32] Stephane Mallat. A Wavelet Tour.of Signal Processing.杨力华,戴道清等.北京:机械工业出版社.2002,182-186.
[33] 任礼斌.小波分析在基于内容的图像检索中的应用研究:[硕士学位论文].沈阳:沈阳工业学院,2003.
[34] S. G. Mallat, A theory for multiresolution signal representation: the wavelet decomposition, IEEE Trans. On Pattern Analysis and Machine Intelligence, 1989, 11 ( 7):674-693.
[35] B. S. Manjunath, W. Y. Ma. Texture features for browsing and retrieval of image data.IEEE Trans. on Pattern Analysis and Machine Intelligence, 1996, 18(8):837-842.
[36] 冯象初,甘小冰,宋国乡.数值泛函与小波理论.西安:西安电子科技大学出版社,2002.
[37] 章毓晋.基于内容的视觉信息检索.北京:科学出版社,2003,104-105.
[38] 孙即祥.模式识别中的特征提取与计算机视觉不变量。北京:国防工业出版社,2001.
[39] T. Meiers, T.Ellerbrock, T. Sikora, Image browsing with PCA-assisted user-interaction.Content-Based Access of Image and Video Libraries,IEEE Workshop on , 2001,102-108.
[40] 韩军伟.基于内容的图象检索技术研究:[博士学位论文].西安:西北工业大学,2003.
[41] 王耀南.计算智能信息处理技术及其应用.长沙:湖南大学出版社,1999, 24-29.
[42] C.Vertan, V.Buzuloiu. Using fuzzy histograms and distances for color image retrieval. In Proc.of CIR’2000, Brighton,United Kingdom, 2000,4-5 .
[43] C.P.Papps, N.I.Karacapilidis. A comparative assessment of measures of similarity of fuzzy values. Fuzzy sets and systems (56), 1993,171-174.
[44] I.Bloch. On fuzzy distances and their use in image processing under imprecision. Pattern Recognition (32),1999 1873-1895.
[45] S-M Chen,M-S Yeh,P-Y Hsiao.A comparison of similarity measures of fuzzy values. Fuzzy sets and systems (72), 1995,79-89.
[46] R.C.Jain,S.Nmurthy, L.Tran.Similarity measures for image database. In proc.Of IEEE Conference on Fuzzy Systems, 1995, 3:1247-1254.
[47] W-J Wang. New similarity measures on fuzzy sets and on elements. Fuzzy Sets and Systems,1997,85:305-309.
[48] M.Granzow, D.Berrar, W.Dubitzky et al. Tumor Classification by Gene Expression Profiling: Comparison and Validation of Five Clustering Methods.ACM SIGBIO Newsletter, 2001,21(1).
[49] J.C.Bezdek. Pattern Recognition with Objective Function Algorithms.New York: Plenum, 1981.43-93.
[50] De Castro L.N.Artificial immune systems as a Novel Soft Computing Paradigm.Soft Computing Journal, 2003, 7(7).
[51] Jerne.N.K.Towards a Network Theory of the Immune Systems.Annual Immunology (1nst, Pasteur) 125-C, 73 –389.
[52] De Castro L.N., Von Zuben F.J.aiNet: An artificial immune network for data analysis. H.A. Abbas, R.A. Sarker, Ch.S. Newton, eds. Data Mining: A Heuristic Approach. USA: Idea Group Publishing, 2001, 231-259
[53] 莫宏伟.人工免疫系统原理与应用.哈尔滨:哈尔滨工业大学出版社.2002.
[54] C.Carson M.Thomas, S.Belongie, J.M. Hellerstein and J. Malik. Blobworld: a system for region-based image indexing and retrieval. Proc. Visual Information Systems, June 1999, 509-516.
[55] J.Z.Wang, J.Li and G.Wiederhold. SIMPLIcity: semantics-sensitive integrated matching for picture libraries. IEEE Trans. on PAMI, 2001,23 (9):1-17.
[56] J. Li, J.Z. Wang, G.Wiederhold. IRM: integrated region matching for image retrieval. Proceeding of ACM Multimedia, 2000.
[57] Y.X.Chen, J.Z.Wang. A Region-based fuzzy feature matching approach to content-based image retrieval. IEEE transactions on pattern analysis and machine intelligence,2002, 24(9),1252-1267.
[58] 梅丽霞, 田玉敏. 基于区域的图像数据库检索方法. 计算机工程. 2003,29(5):127-129.
[59] 黄元元,郭丽,杨静宇. 基于目标区域颜色与纹理特征的图像检索. 南京理工大学学报, 2003,27(3):286-289.
[60] MacQueen J. Some methods for classification and analysis of multivariate observations.Proc.5 Th Berkeley Symp. Math. Statist, Prob[C] 1967, 1:281 -297 .
[61] Huang Z. Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Mining and Knowledge Discovery,1998, 2: 283-304.
[62] A.Gersho. asymptotically optimum block quantization. IEEE Trans Information Theory,1979,25(4):373-380.
[63] 林开颜,徐立鸿,吴军辉. 快速模糊C 均值聚类彩色图像分割方法.中国图象图形学报,2004,9(2).
[64] Chen C C. Improved moment invariant for shape discrimination. Pattern recognition, 1993,26:683~686.
[65] Benny Johansen,Matthew Reynolds 等著,张哲峰译. Windows 应用高级编程—c#编程篇.北京:清华大学出版社,2002.