色度空间上基于子块区域生长的彩色图像分割方法
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摘要
图像分割是图像工程中一项基础而关键的技术。它是任何图像分析过程中首要的任务，因为接下来所要做的(如特征提取，目标识别等)都取决于图像分割的质量。近年来随着机器视觉、模式识别和基于内容的图像检索等技术的不断升温以及彩色图像的大量使用，图像分割特别是彩色图像的分割显示出越来越重要的地位。
本文在比较和分析现有彩色图像分割技术的基础上提出了一种改进的区域生长算法——色度空间上基于子块区域生长的彩色图像分割方法。颜色空间选取与人类视觉一致的HSV空间，在奇异点附近以非彩色信息作色调值的加权值。该方法不但考虑了像素的属性，还考虑了像素群的属性。图像首先被分成合适大小的子块，子块的色彩均值和方差值作为像素群的属性，用基于子块的区域生长来进行图像分割；根据提取出的对象大小以及它们的空间位置关系，去除掉过小的噪声对象同时将有意义的小对象合并到其所属的大对象中；最后处于边界的子块将逐像素地归类到对应的相邻对象中。
为了分析算法的性能，我们用多幅图像对其进行验证并对结果进行了分析。实验表明，该方法可以适合于多种类型的图像并且其计算量远小于传统的基于像素的区域生长方法，同时有助于改善由图像噪声所引起的过分割现象。
Image segmentation is the basic and important technology in Image Engineering. It is a first task of any image analysis process because all the subsequent tasks (feature extraction and object recognition etc.) rely heavily on the quality of the segmentation. In recent years with the development of technologies, such as machine vision, pattern recognition and content-based image retrieve etc, and the wide use of color images, image segmentation, especially color image segmentation, has played a more and more important role.
Many existing methods for image segmentation have been studied and compared in this thesis, and then an improved region growing algorithm is proposed-color image segmentation using chroma space based on block region growing. HSV color space that is consistent with human eyes is selected; achromatic information is used as the weigh on the hue value for strange pixels. Not only the pixel properties but also the pixel group properties are considered. Firstly an image is divided into most suitable blocks. For each block, the mean and variance values can be seen as the pixel group properties. The image segmentation is done with the approach of region growing based on block mean and variance. Considering the size of extracted objects and their relative position information, get rid of the "noise" objects and merge the meaningful fragmentary regions into their corresponding bigger ones. Finally all pixels in non-object blocks are classified into their corresponding adjacent objects.
In order to analysis the performance of the whole algorithm, some pictures are tested and the segmentation results are analyzed. The experiments confirm this
method is suitable for many kinds of images, its complexity of computation is less than traditional pixel-based methods, and also it can help do with the oversegmentation.
本文在比较和分析现有彩色图像分割技术的基础上提出了一种改进的区域生长算法——色度空间上基于子块区域生长的彩色图像分割方法。颜色空间选取与人类视觉一致的HSV空间，在奇异点附近以非彩色信息作色调值的加权值。该方法不但考虑了像素的属性，还考虑了像素群的属性。图像首先被分成合适大小的子块，子块的色彩均值和方差值作为像素群的属性，用基于子块的区域生长来进行图像分割；根据提取出的对象大小以及它们的空间位置关系，去除掉过小的噪声对象同时将有意义的小对象合并到其所属的大对象中；最后处于边界的子块将逐像素地归类到对应的相邻对象中。
为了分析算法的性能，我们用多幅图像对其进行验证并对结果进行了分析。实验表明，该方法可以适合于多种类型的图像并且其计算量远小于传统的基于像素的区域生长方法，同时有助于改善由图像噪声所引起的过分割现象。
Image segmentation is the basic and important technology in Image Engineering. It is a first task of any image analysis process because all the subsequent tasks (feature extraction and object recognition etc.) rely heavily on the quality of the segmentation. In recent years with the development of technologies, such as machine vision, pattern recognition and content-based image retrieve etc, and the wide use of color images, image segmentation, especially color image segmentation, has played a more and more important role.
Many existing methods for image segmentation have been studied and compared in this thesis, and then an improved region growing algorithm is proposed-color image segmentation using chroma space based on block region growing. HSV color space that is consistent with human eyes is selected; achromatic information is used as the weigh on the hue value for strange pixels. Not only the pixel properties but also the pixel group properties are considered. Firstly an image is divided into most suitable blocks. For each block, the mean and variance values can be seen as the pixel group properties. The image segmentation is done with the approach of region growing based on block mean and variance. Considering the size of extracted objects and their relative position information, get rid of the "noise" objects and merge the meaningful fragmentary regions into their corresponding bigger ones. Finally all pixels in non-object blocks are classified into their corresponding adjacent objects.
In order to analysis the performance of the whole algorithm, some pictures are tested and the segmentation results are analyzed. The experiments confirm this
method is suitable for many kinds of images, its complexity of computation is less than traditional pixel-based methods, and also it can help do with the oversegmentation.
引文
[1] 章毓晋．图像处理和分析基础．北京：高等教育出版社．2002．7
[2] 薛景浩，章毓晋，林行刚．SAR图像基于Rayleigh分布假设的最小误差阈值化分析．电子科学学刊，1999，21(2)：219-225
[3] Tian B, Shaikh M A, Azimi-Sadjadi M R.A Study of Cloud Classification with Neural Networks Using Spectral and Textures Features. IEEE Trans. Neutral Networks, 1999,10(1): 138-151
[4] 陈哲，冯天谨．基于小波分形特征提取的图像分割方法．中国图像图形学报，1999，4(12)：1072-1077
[5] Zhang Y, Smith S M, and Brady J M. Segmentation of brain MR image using Markov Random Field. Proceedings of Medical Image Understanding and Analysis'99,http://www. umds.ac.uk/miua/
[6] Marie-Pierre Dubuisson Jolly, Sridhar Lakshmanan, and Anil K. Jain. Vehicle Segmentation and Classification Using Deformable Templates. IEEE Tans. Pattern Analysis and Machine Intelligence, 1996.18(3):293—308
[7] RobKoenen. MPEG-4Overview. http://drogo.saelt.it/mpeg/standards/mpeg-4/mpeg-4.html
[8] Swanson M D,Tewfik A H.Fast Progressively Refined Image Retrieval. Journal of Electronic Imaging, 1998,7(3):443—452
[9] Brink A B. Gray level thresholding of Images using a correlation criterion.Pattern Recognition Letters, 1989,9:335-341
[10] Mardia K V and Hainsworth T J.A spatial thresholding method for Image segmentation.IEEE Trans. Pattern Analysis and Machine Intelligence, 1988, 10:919-237
[11] Run T. A new method for gray level picture thresholding using the entropy of the histogram. Signal Processing, 1980,2:223-237
[12] 靳宏磊，朱蔚萍，李立源，陈维南．二维灰度直方图的最佳分割方法．模式识别与人工智能，1999，12(3)：329-333
[13] 刘文萍，吴立德．图像分割中阈值选取方法比较研究．模式识别与人工智能，1997，10(3)：329-277
[14] Bomana M, Hohne K H, Tiede U, and Rieme M.3-D Segmentation of MR Images of the head for 3-D display. IEEE Trans. On Medical imaging, 1990,9:177-183
[15] Bergholm F. Edge focusing. IEEE Trans. Pattern analysis and Machine Intelligence, 1987,9(9): 726-741
[16] 应骏，叶秀清，顾伟康．一个基于知识的边沿提取算法．中国图像图形学报，1999，4(3)：239-242
[17] Michael H F. Wilkinson. Optimizing Edge Detectors for Robust Automatic Threshold Selection: Coping with Edge Curvature and Noise. Graphical Models and Image Processing, 1998,60:385-401
[18] Ziou D, Tabbone S. A multi-scale edge detector. Pattern Recognition, 1993,26(9): 1305-1314
[19] Brown M A, Blackwell K T, and Khalak H G etal. Multi-scale Edge Detector and Feature Binding: An Integrated Approach. Pattern Recognition, 1998,31(10): 1479-1490
[20] S.A. Hijjatoleslami and J. Kittler. Region growing: A new approach. IEEE Trans. Image Processing, 1998,7:1079-1084
[21] Adams R. and Bischof L. Seeded region growing. IEEE Trans. Pattern Anal& Machine Intell, 1994,16(6) 641-647
[22] Bomberg T and Posch S. Regionensegmentierung von Farbbildfolgen, In: Paulus E and Wahl F.M. /eds./, Mustererkennung 1997, Springer, Berlin 1997, 63-70
[23] Mehnert A and Jackway P. An improved seeded region growing algorithm. Pattern Rec Lett, 1997,18:1065-1071
[24] 王月兰，曾迎生．信息融合技术在彩色图像分割方法中的应用．计算机学报，2000年7月，23(7)：763—767
[25] Sergey N. Krjukov, Tatjana O.Semenkova. Backpropagation neural network for adaptive color image segmentation. SPIE 1997 Vol.3030,70-74
[26] Arthur R. Weeks, G. Eric Hague. Color Segmentation in the HIS color space using the k-means algorithm. SPIE 1997 Vol.3026,143-154
[27] L. Lucchese, S. K. Mitra. Unsupervised segmentation of color images based on k-means clustering in the chromaticity plane. Proc. Of the IEEE Workshop on Content-based Access Image and Video Libraries, 21 June, Santa Barbara, California
[28] Elisaber, A.Murat Tekalp. Region—based shape matching for automatic image annotation and query—by—example. Journal of Visual Communication and Image Representation. 1997,8(1): 3—20
[29] Tremeau A, Borel N A. Region growing and merging algorithm to color segmentation. Pattern Recognition, 1997, 30(7): 1191-1203
[30] N.Ikonomakis, K N. Plataniotis and A N. Venetsanopoulos. A Region-based Color Image Segmentation Scheme. SPIE, 1999, 3653:1202—1209
[31] Henry R. Kang. Color Technology For Electronic Imaging Devices. SPIE-The International Society for Optical Engineering, 1997.
[32] Oliver D. Faugeras. Digital Color Image Processing Within the Framework of a Human Visual Model. IEEE Transactions on acoustics, speech and signal processing, ASSP-27(4)
[33] G.Sapiro. Cromaticity Diffusion. Proc. of ICIP 2000, Vancouver, BC, Canada, Sept,2000(2):784-787
[34] L Lucchese and S.K.Mitra. Unsupervised Segmentation of Color Images Based on k-means Clustering in the Chromaticity Plane. Proc. of CBAIVL'99. Fort Collins, CO, June 1999(7): 74-78
[35] L.Lucchese and S.K.Mitra. Advances in Color Image Segmentation. Proc.of Globecom'99, Rio de Janeiro, Brazil, 1999(11):2038-2044.
[36] J.C.Noordam, W.H.A.M.van den Broek, L.M.C.Buydens. Geometrically
Guided Fuzzy C-means Clustering for Multivariate Image Segmentation. International Conference on Pattern Recognition, ICPR 2000:462-466
[37] Rajesh N. and Raghu K. Robust Clustering Methods: A Unified View, IEEE Trans. On Fuzzy System, 1997, 5 (2): 270-293
[38] I. Vanhamel, I. Pratikakis, H. Sahli. Automatic Watershed segmentation of color images. Proc. Mathematical Morphology and its applications on Image and Signal processing (ISMM'00), Kluwer Academic Press, 2000
[39] L. Naiman and M. Schmitt. Geodesic Saliency of Watershed Contours and Hierarchical Segmentation, IEEE Trans. Pattern Analysis and Machine Intelligence, 1996, 18 (12): 1163-1173
[40] Anil K.Jain. Fundamentals of Digital Image Processing. Prentice-Hall 1989.
[41] Rafael C. Gonzales/PaulWitness. Digital Image Processing. Second edition, ADDISON-WESLEY 1987.
[42] CHI Zhi-gang,Kimura Toshifumi,Yamauchi Kenji etc. Image Segmentation Considering Intensity Roughness and Color Purity. Journal of Software, 2002, 13(5):907-912
[43] 章毓晋．图像工程(上册)——图像处理与分析．北京：清华大学出版社．1999．3
[44] 夏良正．数字图像处理(修订版)．南京：东南大学出版社．1999．9
[45] (美)卡斯尔曼(Castleman,K.R.)著；朱志刚等译．数字图像处理．北京：电子工业出版社．2002．2
[46] 徐飞，施晓红等．MATLAB应用图像处理．西安：西安电子科技大学出版社．2002．5
[47] 魏宝刚，李向阳，鲁东明，潘云鹤．彩色图像分割研究进展．计算机科学，1999，26(4)：59-62
[48] 詹劲峰，戚飞虎，王海龙．基于人眼视觉特征的彩色图像分割方法．计算机工程，2001，27(2)：68—69
[49] 龚声蓉，熊璋，孙文彦．一种基于HSL概率密度的彩色图像分割方法．
小型微型计算机系统，2001，22(6)：695—698
[50] 魏宝刚，鲁东明，潘云鹤等．多颜色空间上的交互式图像分割．计算机学报，2001年，24(7)：770—775
[51] 樊昀，王润生．面向内容检索的彩色图像分割．计算机研究与发展，2002，39(3)：376—381
[52] 蒋苏蓉，王松，冯刚．iRGA—一种改进的区域生长彩色图像分割方法．计算机工程与应用，2003，39(7)：96—98
[2] 薛景浩，章毓晋，林行刚．SAR图像基于Rayleigh分布假设的最小误差阈值化分析．电子科学学刊，1999，21(2)：219-225
[3] Tian B, Shaikh M A, Azimi-Sadjadi M R.A Study of Cloud Classification with Neural Networks Using Spectral and Textures Features. IEEE Trans. Neutral Networks, 1999,10(1): 138-151
[4] 陈哲，冯天谨．基于小波分形特征提取的图像分割方法．中国图像图形学报，1999，4(12)：1072-1077
[5] Zhang Y, Smith S M, and Brady J M. Segmentation of brain MR image using Markov Random Field. Proceedings of Medical Image Understanding and Analysis'99,http://www. umds.ac.uk/miua/
[6] Marie-Pierre Dubuisson Jolly, Sridhar Lakshmanan, and Anil K. Jain. Vehicle Segmentation and Classification Using Deformable Templates. IEEE Tans. Pattern Analysis and Machine Intelligence, 1996.18(3):293—308
[7] RobKoenen. MPEG-4Overview. http://drogo.saelt.it/mpeg/standards/mpeg-4/mpeg-4.html
[8] Swanson M D,Tewfik A H.Fast Progressively Refined Image Retrieval. Journal of Electronic Imaging, 1998,7(3):443—452
[9] Brink A B. Gray level thresholding of Images using a correlation criterion.Pattern Recognition Letters, 1989,9:335-341
[10] Mardia K V and Hainsworth T J.A spatial thresholding method for Image segmentation.IEEE Trans. Pattern Analysis and Machine Intelligence, 1988, 10:919-237
[11] Run T. A new method for gray level picture thresholding using the entropy of the histogram. Signal Processing, 1980,2:223-237
[12] 靳宏磊，朱蔚萍，李立源，陈维南．二维灰度直方图的最佳分割方法．模式识别与人工智能，1999，12(3)：329-333
[13] 刘文萍，吴立德．图像分割中阈值选取方法比较研究．模式识别与人工智能，1997，10(3)：329-277
[14] Bomana M, Hohne K H, Tiede U, and Rieme M.3-D Segmentation of MR Images of the head for 3-D display. IEEE Trans. On Medical imaging, 1990,9:177-183
[15] Bergholm F. Edge focusing. IEEE Trans. Pattern analysis and Machine Intelligence, 1987,9(9): 726-741
[16] 应骏，叶秀清，顾伟康．一个基于知识的边沿提取算法．中国图像图形学报，1999，4(3)：239-242
[17] Michael H F. Wilkinson. Optimizing Edge Detectors for Robust Automatic Threshold Selection: Coping with Edge Curvature and Noise. Graphical Models and Image Processing, 1998,60:385-401
[18] Ziou D, Tabbone S. A multi-scale edge detector. Pattern Recognition, 1993,26(9): 1305-1314
[19] Brown M A, Blackwell K T, and Khalak H G etal. Multi-scale Edge Detector and Feature Binding: An Integrated Approach. Pattern Recognition, 1998,31(10): 1479-1490
[20] S.A. Hijjatoleslami and J. Kittler. Region growing: A new approach. IEEE Trans. Image Processing, 1998,7:1079-1084
[21] Adams R. and Bischof L. Seeded region growing. IEEE Trans. Pattern Anal& Machine Intell, 1994,16(6) 641-647
[22] Bomberg T and Posch S. Regionensegmentierung von Farbbildfolgen, In: Paulus E and Wahl F.M. /eds./, Mustererkennung 1997, Springer, Berlin 1997, 63-70
[23] Mehnert A and Jackway P. An improved seeded region growing algorithm. Pattern Rec Lett, 1997,18:1065-1071
[24] 王月兰，曾迎生．信息融合技术在彩色图像分割方法中的应用．计算机学报，2000年7月，23(7)：763—767
[25] Sergey N. Krjukov, Tatjana O.Semenkova. Backpropagation neural network for adaptive color image segmentation. SPIE 1997 Vol.3030,70-74
[26] Arthur R. Weeks, G. Eric Hague. Color Segmentation in the HIS color space using the k-means algorithm. SPIE 1997 Vol.3026,143-154
[27] L. Lucchese, S. K. Mitra. Unsupervised segmentation of color images based on k-means clustering in the chromaticity plane. Proc. Of the IEEE Workshop on Content-based Access Image and Video Libraries, 21 June, Santa Barbara, California
[28] Elisaber, A.Murat Tekalp. Region—based shape matching for automatic image annotation and query—by—example. Journal of Visual Communication and Image Representation. 1997,8(1): 3—20
[29] Tremeau A, Borel N A. Region growing and merging algorithm to color segmentation. Pattern Recognition, 1997, 30(7): 1191-1203
[30] N.Ikonomakis, K N. Plataniotis and A N. Venetsanopoulos. A Region-based Color Image Segmentation Scheme. SPIE, 1999, 3653:1202—1209
[31] Henry R. Kang. Color Technology For Electronic Imaging Devices. SPIE-The International Society for Optical Engineering, 1997.
[32] Oliver D. Faugeras. Digital Color Image Processing Within the Framework of a Human Visual Model. IEEE Transactions on acoustics, speech and signal processing, ASSP-27(4)
[33] G.Sapiro. Cromaticity Diffusion. Proc. of ICIP 2000, Vancouver, BC, Canada, Sept,2000(2):784-787
[34] L Lucchese and S.K.Mitra. Unsupervised Segmentation of Color Images Based on k-means Clustering in the Chromaticity Plane. Proc. of CBAIVL'99. Fort Collins, CO, June 1999(7): 74-78
[35] L.Lucchese and S.K.Mitra. Advances in Color Image Segmentation. Proc.of Globecom'99, Rio de Janeiro, Brazil, 1999(11):2038-2044.
[36] J.C.Noordam, W.H.A.M.van den Broek, L.M.C.Buydens. Geometrically
Guided Fuzzy C-means Clustering for Multivariate Image Segmentation. International Conference on Pattern Recognition, ICPR 2000:462-466
[37] Rajesh N. and Raghu K. Robust Clustering Methods: A Unified View, IEEE Trans. On Fuzzy System, 1997, 5 (2): 270-293
[38] I. Vanhamel, I. Pratikakis, H. Sahli. Automatic Watershed segmentation of color images. Proc. Mathematical Morphology and its applications on Image and Signal processing (ISMM'00), Kluwer Academic Press, 2000
[39] L. Naiman and M. Schmitt. Geodesic Saliency of Watershed Contours and Hierarchical Segmentation, IEEE Trans. Pattern Analysis and Machine Intelligence, 1996, 18 (12): 1163-1173
[40] Anil K.Jain. Fundamentals of Digital Image Processing. Prentice-Hall 1989.
[41] Rafael C. Gonzales/PaulWitness. Digital Image Processing. Second edition, ADDISON-WESLEY 1987.
[42] CHI Zhi-gang,Kimura Toshifumi,Yamauchi Kenji etc. Image Segmentation Considering Intensity Roughness and Color Purity. Journal of Software, 2002, 13(5):907-912
[43] 章毓晋．图像工程(上册)——图像处理与分析．北京：清华大学出版社．1999．3
[44] 夏良正．数字图像处理(修订版)．南京：东南大学出版社．1999．9
[45] (美)卡斯尔曼(Castleman,K.R.)著；朱志刚等译．数字图像处理．北京：电子工业出版社．2002．2
[46] 徐飞，施晓红等．MATLAB应用图像处理．西安：西安电子科技大学出版社．2002．5
[47] 魏宝刚，李向阳，鲁东明，潘云鹤．彩色图像分割研究进展．计算机科学，1999，26(4)：59-62
[48] 詹劲峰，戚飞虎，王海龙．基于人眼视觉特征的彩色图像分割方法．计算机工程，2001，27(2)：68—69
[49] 龚声蓉，熊璋，孙文彦．一种基于HSL概率密度的彩色图像分割方法．
小型微型计算机系统，2001，22(6)：695—698
[50] 魏宝刚，鲁东明，潘云鹤等．多颜色空间上的交互式图像分割．计算机学报，2001年，24(7)：770—775
[51] 樊昀，王润生．面向内容检索的彩色图像分割．计算机研究与发展，2002，39(3)：376—381
[52] 蒋苏蓉，王松，冯刚．iRGA—一种改进的区域生长彩色图像分割方法．计算机工程与应用，2003，39(7)：96—98
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