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Color image segmentation: advances and prospects. (English) Zbl 0991.68137
Summary: Image segmentation is very essential and critical to image processing and pattern recognition. This survey provides a summary of color image segmentation techniques available now. Basically, color segmentation approaches are based on monochrome segmentation approaches operating in different color spaces. Therefore, we first discuss the major segmentation approaches for segmenting monochrome images: histogram thresholding, characteristic feature clustering, edge detection, region-based methods, fuzzy techniques, neural networks, etc.; then review some major color representation methods and their advantages/disadvantages; finally summarize the color image segmentation techniques using different color representations. The usage of color models for image segmentation is also discussed. Some novel approaches such as fuzzy method and physics-based method are investigated as well.

MSC:
68U10Image processing (computing aspects)
68T05Learning and adaptive systems
68T10Pattern recognition, speech recognition
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References:
[1] Pal, S. K.: A review on image segmentation techniques. Pattern recognition 29, 1277-1294 (1993)
[2] Fu, K. S.; Mui, J. K.: A survey on image segmentation. Pattern recognition 13, 3-16 (1981)
[3] Riseman, E. M.; Arbib, M. A.: Computational techniques in the visual segmentation of static scenes. Comput. vision graphics image process. 6, 221-276 (1977)
[4] W. Skarbek, A. Koschan, Colour image segmentation -- a survey, Technical Report, Tech. Univ. of Berlin, October 1994.
[5] Haralick, R. M.; Shapiro, L. G.: Image segmentation techniques. Comput. vision graphics image process. 29, 100-132 (1985)
[6] Sahoo, P. K.: A survey of thresholding techniques. Comput. vision graphics image process. 41, 233-260 (1988)
[7] L. Spirkovska, A summary of image segmentation techniques, NASA Technical Memorandum 104022, June 1993.
[8] C.H. Chen, On the statistical image segmentation techniques, Proceedings of IEEE Conference on Pattern Recognition and Image Processing, 1981, pp. 262--266.
[9] Rosenfeld, A.; Thurston, M.: Edge and curve detection for visual scene analysis. IEEE trans. Comput. 20, 562-569 (1971)
[10] Rosenfeld, A.; Thurston, M.; Lee, Y.: Edge and curve detection further experiments. IEEE trans. Comput. 21, 677-715 (1972) · Zbl 0235.68039
[11] Hueckel, M.: An operator which locates edges in digitized pictures. J. assoc. Comput. Mach 18, No. 1, 113-125 (1971) · Zbl 0219.68061
[12] Hueckel, M.: A local visual operator which recognizes edges and lines. J. assoc. Comput. Mach 20, No. 4, 634-647 (1973) · Zbl 0273.68071
[13] J. Gauch, Chi-Wan Hsia, A comparison of three color image segmentation algorithm in four color spaces, SPIE Vol. 1818 Visual Communications and Image Processing ’92, 1992, pp. 1168--1181.
[14] Hoy, D. E. P.: On the use of color imaging in experimental applications. Exp. tech. 21, No. 4, 17-19 (1997)
[15] M. Chapron, A new chromatic edge detector used for color image segmentation, IEEE International Conference on Pattern Recognition, A, 1992, pp. 311--314.
[16] Orchard, M. T.; Bouman, C. A.: Color quantization of images. IEEE trans. Signal process. 39, No. 12, 2677-2690 (1991)
[17] D. Comaniciu, P. Meer, Robust analysis of feature spaces: color image segmentation, IEEE Conference on Computer Vision and Pattern Recognition, 1997, pp. 750--755.
[18] M. Pietikainen et al., Accurate color discrimination with classification based on feature distributions International Conference on Pattern Recognition, C, 1996, pp. 833--838.
[19] Littmann, E.; Ritter, H.: Adaptive color segmentation -- a comparison of neural and statistical methods. IEEE trans. Neural network 8, No. 1, 175-185 (1997)
[20] Robinson, G. S.: Color edge detection. Opt. eng. 16, No. 5, 479-484 (1977)
[21] Ohta, Y.; Kanade, T.; Sakai, T.: Color information for region segmentation. Comput. graphics image process. 13, 222-241 (1980)
[22] Golland, P.; Bruckstein, A. M.: Why R.G.B.? or how to design color displays for martians. Graphical models image process. 58, No. 5, 405-412 (1996)
[23] Nevatia, A color edge detector and its use in scene segmentation, IEEE Trans. System Man Cybernet. SMC-7 (11) (1977) 820--826.
[24] Andreadis, I.; Browne, M. A.; Swift, J. A.: Image pixel classification by chromaticity analysis. Pattern recognition lett. 11, 51-58 (1990) · Zbl 0800.68803
[25] J.C. Terrillon, M. David, S. Akamatsu, Detection of human faces in complex scene images by use of a skin color model and of invariant Fourier-Mellin moments, IEEE International Conference on Pattern Recognition, 1998, pp. 1350--1355.
[26] Huntsberger, T. L.; Jacobs, C. L.; Cannon, R. L.: Iterative fuzzy image segmentation. Pattern recognition 18, No. 2, 131-138 (1985)
[27] T. Carron, P. Lambert, Color edge detector using jointly hue, saturation and intensity, IEEE International Conference on Image Processing, Austin, USA, 1994, pp. 977--1081.
[28] Y. Rui, A.C. She, T.S. Huang, Automated region segmentation using attraction-based grouping in spatial-color-texture space, International Conference on Image Processing, A, 1996, pp. 53--56.
[29] 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, C, 1996, pp. 1041--1044.
[30] P.W.M. Tsang, W.H. Tsang, Edge detection on object color, IEEE International Conference on Image Processing, C, 1996, pp. 1049--1052.
[31] Tepichin, E.; Suarez-Romero, J. G.; Ramirez, G.: Hue, brightness, and saturation manipulation of diffractive colors. Opt. eng. 34, No. 10, 2886-2890 (1995)
[32] 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, pp. 1035--1039.
[33] D.C. Tseng, C.H. Chang, Color segmentation using perceptual attributes, IEEE International Conference on Pattern Recognition, A, 1992, pp. 228--231.
[34] Tominaga, S.: Expansion of color images using three perceptual attributes. Pattern recognition lett. 6, 77-85 (1987)
[35] N. Vandenbroucke, L. Macaire, J.G. Posaire, Color pixels classification in an hybrid color space, IEEE International Conference on Image Processing, 1998, pp. 176--180.
[36] Shafer, S. A.: Using color to separate reflection components. Color res. Appl. 10, No. 4, 210-218 (1985)
[37] Healey, G.: Using color for geometry-insensitive segmentation. Opt. soc. Am. 22, No. 1, 920-937 (1989)
[38] J. Kender, Saturation, hue, and normalized color: calculation, digitization effects, and use, Computer Science Technical Report, Carnegie Mellon University, 1976.
[39] Yang, C. K.; Tsai, W. H.: Reduction of color space dimensionality by moment-preserving thresholding and its application for edge detection in color images. Pattern recognition lett. 17, 481-490 (1996)
[40] Klinker, G. J.; Shafer, S. A.; Kanade, T.: A physical approach to color image understanding. Int. J. Comput. vision 4, 7-38 (1990)
[41] Brill, M. H.: Image segmentation by object color: a unifying framework and connection to color constancy. Opt. soc. Am. 7, No. 10, 2041-2047 (1990)
[42] Healey, G.: Segmenting images using normalized color. IEEE trans. System man cybernet. 22, No. 1, 64-73 (1992)
[43] Petrov, A. P.; Kontsevich, L. L.: Properties of color images of surfaces under multiple illuminants. Opt. soc. Am. 11, No. 10, 2745-2749 (1994)
[44] B.A. Maxwell, S.A. Shafer, Physics-based segmentation: moving beyond color, IEEE International Conference on Computer Vision and Pattern Recognition, 1996, pp. 742--749.
[45] G.J. Klinker, S.A. Shafer, Takeo Kanade, Image segmentation and reflection analysis through color, Proceedings of the Image Understanding Workshop, Morgan Kaufmann, San Mateo, CA, 1988, pp. 835--838.
[46] Guth, S. L.: Model for color vision and light adaptation. Opt. soc. Am. 8, No. 6, 976-993 (1991)
[47] Shafer, S. A.; Kanade, T.: Using shadows in finding surface orientations. Comput. vision graphics image process. 22, 145-176 (1983)
[48] Klinker, G. J.; Shafer, S. A.; Kanade, T.: The measurement of highlights in color images. Int. J. Comput. vision 2, No. 1, 7-32 (1988)
[49] S. Shafer, T. Kanade, G. Klinker, C. Novak, Physics-based models for early vision by machine SPIE, Perceiving, Measuring, and Using Color, Vol. 1250, Santa Clara, February 1990, pp. 222--235.
[50] Bajcsy, R.; Wooklee, S.; Leonardis, A.: Detection of diffuse and specular interface reflections and inter-reflections by color image segmentation. Int. J. Comput. vision 17, No. 3, 241-272 (1996)
[51] M.H. Yang, N. Ahuja, Detecting human faces in color images, IEEE International Conference on Image Processing, 1998, pp. 127--130.
[52] B.A. Maxwell, S.A. Shafer, Physics-based segmentation: looking beyond color, IEEE Computer Vision and Pattern Recognition, June 16--20, 1996, pp. 867--878.
[53] W. Power, R. Clist, Comparison of supervised learning techniques applied to color segmentation of fruit image, Proceedings of SPIE, Intelligent Roberts and Computer Vision XV: Algorithms, Techniques, Active Vision, and Materials Handling, Boston, Massachusetts, November 19--21, 1996, pp. 370--381.
[54] Hance, G. A.; Umbaugh, S. E.; Moss, R. H.; Stoecker, W. V.: Unsupervised color image segmentation with application to skin tumor borders. IEEE eng. Med. biol. 15, No. 1, 104-111 (1996)
[55] Wu, J.; Yan, H.; Chalmers, A. N.: Color image segmentation using fuzzy clustering and supervised learning. J. electron. Image 3, No. 4, 397-403 (1994)
[56] Eom, K. B.: Segmentation of monochrome and color texture using moving average modeling approach. Image vision comput. 17, 233-244 (1999)
[57] Uchiyama, T.; Arbib, M. A.: Color image segmentation using competitive learning. IEEE trans. Pattern anal. Mach intell. 16, No. 12, 1197-1206 (1994)
[58] B. Schacter, L. Davis, A. Rosenfeld, Scene segmentation by cluster detection in color space, Department of Computer Science, University of Maryland, College Park, MD, November 1975.
[59] Sarabi, A.; Aggarwal, J. K.: Segmentation of chromatic images. Pattern recognition 13, No. 6, 417-427 (1981)
[60] Underwood, S. A.; Aggarwal, J. K.: Interactive computer analysis of aerial color infrared photographs. Comput. graphics image process. 6, 1-24 (1977)
[61] J.M. Tenenbaum, T.D. Garvey, S. Weyl, H.C. Wolf, An interactive facility for scene analysis research, Technical Note 87, Artificial Intelligence Center, Stanford Research Institute, Menlo Park, CA, 1974, 230--239.
[62] Ohlander, R.; Price, K.; Reddy, D. R.: Picture segmentation using a recursive region splitting method. Comput. graphics image process. 8, 313-333 (1978)
[63] S. Tominaga, Color image segmentation using three perceptual attributes, IEEE Proceedings of the Conference on Computer Vision and Pattern Recognition, Los Alamos, CA, 1986, pp. 628--630.
[64] G.D. Guo, S. Yu, S.D. Ma, Unsupervised segmentation of color images, IEEE International Conference on Image Processing, 1998, pp. 299--302.
[65] Celenk, M.: A color clustering technique for image segmentation. Graphical models image process. 52, No. 3, 145-170 (1990)
[66] Tominaga, S.: Color classification of natural color images. Color res. Appl. 17, No. 4 (1992)
[67] Lim, Y. W.; Lee, S. U.: On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques. Pattern recognition 23, No. 9, 935-952 (1990)
[68] Ferri, F.; Vidal, E.: Color image segmentation and labeling through multi-edit condensing. Pattern recognition lett. 13, 561-568 (1992)
[69] Tremeau, A.; Borel, N.: A region growing and merging algorithm to color segmentation. Pattern recognition 30, No. 7, 1191-1203 (1997)
[70] H.D. Cheng, Y. Sun, A hierarchical approach to color image segmentation using homogeneity, IEEE Trans. Image Process. (2001), in press.
[71] L. Macaire, V. Ultre, J.-G. Postaire, Determination of compatibility coefficients for color edge detection by relaxation, International Conference on Image Processing, C, 1996, pp. 1045--1048.
[72] Trahanias, P. E.; Venetsanopoulos, A. N.: Color edge detection using vector order statistics. IEEE trans image process. 2, No. 2, 259-265 (1993)
[73] Trahanias, P. E.; Venetsanopoulos, A. N.: Vector order statistics operators as color edge detectors. IEEE trans. Systems man cybernet.-part B: cybernetics 26, No. 1, 135-143 (1996)
[74] Perez, F.; Koch, C.: Toward color image segmentation in analog VLSI: algorithm and hardware. Int. J. Comput. vision 12, No. 1, 17-42 (1994)
[75] S. Ji, H.W. Park, Image segmentation of color image based on region coherency, IEEE International Conference on Image Processing, 1998, pp. 80--83.
[76] J. Luo, R.T. Gray, H.C. Lee, Incorporation of derivative priors in adaptive bayesian color image segmentation, IEEE International Conference on Image Processing, 1998, pp. 780--784.
[77] Canny, J.: A computational approach to edge detection. IEEE trans. Pattern anal. Mach intell. 8, No. 6, 679-698 (1986)
[78] Ito, N.: The combination of edge detection and region extraction in non-parametric color image segmentation. Inform. sci. 92, 277-294 (1996)
[79] Y. Xiaohan, J. Yla, Image segmentation combining region growing with edge detection, 11th International Conference on Pattern Recognition, The Netherland, August 30--September 3 1992, pp. 481--484.
[80] Pavlidis, T.; Liow, Y. T.: Integrating region growing and edge detection. IEEE trans. Pattern anal. Mach intell. 12, No. 3, 225-233 (1990)
[81] Q. Huang et al., Foreground/background segmentation of color images by integration of multiple cues, International Conference on Image Processing, A, 1995, pp. 246--249.
[82] Q. Huang, B. Dom, N. Megiddo, W. Niblack, Segmenting and representing background in color images, International Conference on Pattern Recognition, C, 1996, pp. 13--17.
[83] R.I. Taylor, P.H. Lewis, Color image segmentation using boundary relaxation, IEEE International Conference on Pattern Recognition, C, 1992, pp. 721--724.
[84] Keller, J. M.; Gray, M. R.; Givens, J. A.: A fuzzy K-nearest neighbor algorithm. IEEE trans. Systems sci. Cybernet. 15, 580-585 (1985)
[85] Keller, J. M.; Carpenter, C. L.: Image segmentation in the presence of uncertainty. Int. J. Intell. systems 15, 193-208 (1990) · Zbl 0715.68096
[86] Huang, L. K.; Wang, M. J.: Image thresholding by minimizing the measures of fuzziness. Pattern recognition 28, No. 1, 41-51 (1995)
[87] De Luca, A.; Termini, S.: A definition of a nonprobabilistic entropy in the setting of fuzzy set theory. Inform. control 20, 301-312 (1972) · Zbl 0239.94028
[88] Pal, N. R.; Pal, S. K.: Object-background segmentation using new definition of entropy. IEE proc. Part E 136, No. 4, 284-295 (1989)
[89] Windham, M. P.: Geometrical fuzzy clustering algorithms. Fuzzy sets and systems 10, 271-279 (1983) · Zbl 0526.68088
[90] Pal, S. K.; King, R. A.: On edge detection of X-ray images using fuzzy sets. IEEE trans. Pattern anal. Mach intell. 5, No. 1, 69-77 (1983)
[91] Chun, D. N.; Yang, H. S.: Robust image segmentation using genetic algorithm with a fuzzy measure. Pattern recognition 29, No. 7, 1195-1211 (1996)
[92] Pal, S. K.; Rosenfeld, A.: Image enhancement and thresholding by optimization of fuzzy compactness. Pattern recognition lett. 7, 77-86 (1988) · Zbl 0709.68518
[93] Tamura, S.; Higuchi, S.; Tanaka, K.: Pattern classification based on fuzzy relations. IEEE trans. System man cybernet 1, No. 1, 61-66 (1971) · Zbl 0224.68012
[94] Cannon, R. L.; Dave, J. V.; Bezdek, J. C.: Efficient implementation of the fuzzy c-means clustering algorithms. IEEE trans. Pattern anal. Mach intell. 8, No. 2, 249-255 (1986) · Zbl 0602.68084
[95] Bezdek, J. C.; Castelaz, P. F.: Prototype classification and feature selection with fuzzy sets. Pattern recognition lett. 14, 483-488 (1993)
[96] Geva, Gath A. B.: Unsupervised optimal fuzzy clustering. IEEE trans. Pattern anal. Mach intell. 11, No. 7, 773-781 (1989) · Zbl 0709.62592
[97] Huntsberger, T. L.; Rangarajan, C.; Jayaramamurphy, S. N.: Representation of the uncertainty in computer vision using fuzzy sets. IEEE trans. Comput. 35, No. 2, 145-156 (1993)
[98] Windham, M. P.: Geometrical fuzzy clustering algorithms. Fuzzy sets and systems 10, 271-279 (1983) · Zbl 0526.68088
[99] Pal, S. K.; King, R. A.: Prototype classification and feature selection with fuzzy sets. Electron. lett. 16, No. 10, 376-378 (1993)
[100] Xie, X. L.; Beni, G.: A validity measure for fuzzy clustering. IEEE trans. Pattern anal. Mach intell. 13, No. 8, 841-847 (1991)
[101] Udupa, J. K.; Samarasekera, S.: Fuzzy connectedness and object definition: theory, algorithms and applications in image segmentation. Graphical models image process. 58, No. 3, 246-261 (1996)
[102] Bloch, I.: Fuzzy connectivity and mathematical morphology. Pattern recognition lett. 14, 483-488 (1993) · Zbl 0781.68129
[103] Rosenfeld, A.: Fuzzy digital topology. Inform. control 40, No. 1, 76-87 (1979) · Zbl 0404.68071
[104] Rosenfeld, A.: The fuzzy geometry of image subsets. Pattern recognition lett. 2, 311-317 (1984)
[105] Tsuda, K.; Minoh, M.; Ikeda, K.: Extracting straight lines by sequential fuzzy clustering. Pattern recognition lett. 17, 643-649 (1996)
[106] Selim, S. Z.; Ismail, M. A.: On the local optimality of the fuzzy isodata clustering algorithm. IEEE trans. Pattern anal. Mach intell. 8, No. 2, 284-288 (1986) · Zbl 0602.68085
[107] Pal, S. K.: Image segmentation using fuzzy correlation. Inform. sci. 62, 223-250 (1992) · Zbl 0764.68189
[108] Moghaddamzadeh, A.; Bourbakis, N.: A fuzzy region growing approach for segmentation of color images. Pattern recognition 30, No. 6, 867-881 (1997)
[109] T. Carron, P. Lambert, Fuzzy color edge extraction by inference rules quantitative study and evaluation of performances, International Conference on Image Processing, B, 1995, pp. 181--184.
[110] H.D. Cheng, J. Li, Fuzzy homogeneity and scale space approach to color image segmentation, International Conference on Computer Vision, Pattern Recognition and Image Processing, Atlantic City, February 27--March 3, 2000.
[111] H.D. Cheng, X.H. Jiang, Homogram thresholding approach to color image segmentation, International Conference on Computer Vision, Pattern Recognition and Image Processing, Atlantic City, February 27--March 3, 2000.
[112] T. Carron, P. Lambert, Symbolic fusion of hue-chroma-intensity features for region segmentation, International Conference on Image Processing, B, 1996, pp. 971--974.
[113] Pienkowski, A. E.; Dennis, T. J.: Applications of fuzzy logic to artificial color vision. SPIE comput. Vision robots 595, 50-55 (1985)
[114] Pedrycz, W.: Fuzzy sets in pattern recognition: methodology and methods. Pattern recognition 23, No. 1/2, 121-146 (1990)
[115] C.W. Tao, W.E. Thompson, A fuzzy if--then approach to edge detection. FUZZY-IEEE 93, San-Francisco, USA, 1993, pp. 1356--1360.
[116] A. Moghaddamzadeh, N. Bourbakis, A fuzzy technique for image segmentation of color images, IEEE Word Congress on Computational Intelligence: FUZZY-IEEE, Orlando, Florida, June 1994.
[117] Y.S. Chen, H.Y. Hwang, B.T. Chen, Color image analysis using fuzzy set theory, International Conference on Image Processing, A, 1995, pp. 242--245.
[118] A. Moghaddamzadeh, N.G. Bourbakis, Segmentation of color images with highlights and shadows using fuzzy reasoning, IS & T/SPIEs Symposium, Electronic Imaging: Science and Technology, San Jose, CA, February 1995, pp. 5--10.
[119] A. Moghaddamzadeh, N. Bourbakis, A fuzzy approach for smoothing and edge detection in color images, IS & T/SPIE Symposium, Electronic Imaging: Science and Technology, San Jose, CA, February 1995.
[120] M. Mari, S. Dellepiane, A segmentation method based on fuzzy topology and clustering, IEEE International Conference on Pattern Recognition, B, 1996, pp. 565--569.
[121] C.K. Ong, T. Matsuyama, Robust color segmentation using the dichromatic reflection model, IEEE International Conference on Pattern Recognition, 1998, pp. 780--784.
[122] Huang, C. -L: Pattern image segmentation using modified Hopfield model. Pattern recognition lett. 13, 345-353 (1999)
[123] Campadelli, P.; Medici, D.; Schettini, R.: Color image segmentation using Hopfield networks. Image vision comput. 15, 161-166 (1997)
[124] M. Sammouda, R. Sammouda, N. Niki, K. Mukai, Segmentation and analysis of liver cancer pathological color image based on artificial neural networks, IEEE 1999 International Conference on Image Processing, October 24--28, 1999, Kobe, Japan, pp. 392--396.
[125] Sammouda, R.; Niki, N.; Nishitani, H.: Segmentation of sputum color image for lung cancer diagnosis based on neural networks. IEICE trans. Inform. systems 81-D, No. 8, 862-871 (1998)
[126] Iwata, H.; Nagahashi, H.: Active region segmentation of color images using neural networks. Systems comput. J. 29, No. 4, 1-10 (1998)
[127] Vesanto, J.; Alhoniemi, E.: Clustering of the self-organizing map. IEEE trans. Neural networks 11, No. 3, 586-600 (2000)
[128] S. Ji, H.W. Park, Image segmentation of color image based on region coherency, 1998 International Conference on Image Processing, Chicago, Illinois, USA, October 4--7 1998, pp. 80--83.
[129] Y.S. Lo, S.C. Pei, Color image segmentation using local histogram and self-organization of Kohonen feature map, 1999 International Conference on Image Processing, KOBE, Japan, October 24--28 1999, pp. 232--239.
[130] P. Lescure, V. Meas-Yedid, H. Dupoisot, G. Stamon, Color segmentation on biological microscope images, Proceeding of SPIE, Application of Artificial Neural Networks in Image Processing IV, San Jose, California, January 28--29, 1999, pp. 182--193.
[131] Rae, R.; Ritter, H. J.: Recognition of human head orientation based on artificial neural networks. IEEE trans. Neural network 9, No. 2, 257-265 (1998)
[132] Ho, C. Y.; Kurokawa, H.: A learning algorithm for oscillatory cellular neural networks. Neural networks 12, 825-836 (1999)
[133] F. Kurugollu, B. Sankur, MAP segmentation of color images using constraint satisfaction neural network, IEEE 1999 International Conference on Image Processing, KOBE, Japan, October 24--28 1999, pp. 236--239.
[134] Schettini, R.: A segmentation algorithm for color images. Pattern recognition lett. 14, 499-506 (1993)
[135] Huang, C. L.; Cheng, T. Y.; Chen, C. C.: Color images segmentation using scale space filter and Markov random field. Pattern recognition 25, No. 10, 1217-1229 (1992)
[136] Sun, Y. N.; Wu, C. S.; Lin, X. Z.; Chou, N. H.: Color image analysis for liver tissue classification. Opt. eng. 32, No. 7, 1609-1614 (1993)
[137] Liu, J. Q.; Yang, Y. H.: Multiresolution color image segmentation. IEEE trans. Pattern anal. Mach intell. 16, No. 7, 689-700 (1994)
[138] Caelli, T.; Reye, D.: On the classification of image regions by color, texture and shape. Pattern recognition 26, No. 4, 461-470 (1993)
[139] K. Valkealahti, E. Oja, Reduced multidimensional histograms in color texture description, International Conference on Pattern Recognition, 1998, pp. 1057--1061.