Genetic-Based Multi Resolution Noisy Color Image Segmentation
Segmentation of a color image composed of different
kinds of regions can be a hard problem, namely to compute for an
exact texture fields. The decision of the optimum number of
segmentation areas in an image when it contains similar and/or un
stationary texture fields. A novel neighborhood-based segmentation
approach is proposed. A genetic algorithm is used in the proposed
segment-pass optimization process. In this pass, an energy function,
which is defined based on Markov Random Fields, is minimized. In
this paper we use an adaptive threshold estimation method for image
thresholding in the wavelet domain based on the generalized
Gaussian distribution (GGD) modeling of sub band coefficients. This
method called Normal Shrink is computationally more efficient and
adaptive because the parameters required for estimating the threshold
depend on sub band data energy that used in the pre-stage of
segmentation. A quad tree is employed to implement the multi
resolution framework, which enables the use of different strategies at
different resolution levels, and hence, the computation can be
accelerated. The experimental results using the proposed
segmentation approach are very encouraging.
[1] R. M. Haralick and L. G. Shapiro, "Image Segmentation Techniques,"
CVGIP, Vol. 29, No. 1, pp. 100-132, January, 1985.
[2] N. R. Pal and S. K. Pal, "A Review on Image Segmentation Techniques,
" Pattern Recognition, Vol. 26, No. 9, pp. 1277-1294, September, 1993.
[3] M. Celenk, "A Color Clustering Technique for Image Segmentation,"
CVGIP, Vol. 52, No. 2, pp. 145-170, November, 1990.
[4] Bhandarkar, S.M., Zhang, Y. and Potter, W.D., 1994, "An Edge
Detection Technique using Genetic Algorithm-based Optimisation",
Pattern Recognition 27(9), pp. 1159-1180.
[5] B. Bhanu, S. Lee, and J. Ming, "Adaptive Image Segmentation Using a
Genetic Algorithm," IEEE Trans on Systems, Man and Cybernetics, Vol.
25, No. 12, pp. 15431567, December, 1995.
[6] D. N. Chun and H. S. Yang, "Robust Image Segmentation Using Genetic
Algorithm with a Fuzzy Measure," Pattern Recognition, Vol. 29, No. 7,
pp. 1195-1211, July, 1996.
[7] H. Derin and H. Elliott, "Modeling and Segmentation of Noisy and
Textured Images Using Gibbs Random Fields,"IEEE Trans. on PAMI,
Vol. 9, No. 1, pp. 39-55, January, 1987.
[8] J. Liu and Y. H. Yang, "Multi resolution Color Image Segmentation,"
IEEE Trans. on PAMI, Vol. 16, No. 7, pp. 689-700, July, 1994.
[9] S. H. Park, I. D. Yun, and S. U. Lee, "Color Image Segmentation Based
on 3-D Clustering," Pattern Recognition, Vol. 31, No. 8, pp. 1061-1076,
August, 1998.
[10] Andrey, P., 1999, "Selections Relaxation: Genetic Algorithms applied to
Image Segmentation", Image and Vision Computing 17, pp. 175-187.
[11] D. E. Goldberg, Genetic Algorithms in Search, Optimization, and
Machine Learning, Addison-Wesley, Reading, MA, USA, 1989.
[12] Chun, D.N. and Yang., H.S., 1996, "Robust Image Segmentation using
Genetic Algorithm with a Fuzzy Measure", Pattern Recognition 29(7),
pp. 1195-1211.
[13] S. M. Bhandarkar and H. Zhang, "Image Segmentation Using
Evolutionary Computation," IEEE Trans. on Evolutionary Comp., Vol.
3, No. 1, pp. 1-21, April, 1999.
[14] H. J. Kim, E. Y. Kim, J. W. Kim, and S. H. Park, "MRF Model Based
Image Segmentation Using Hierarchical Distributed Genetic
Algorithm," Electronics Letters, Vol. 34, No. 25, pp. 2394-2395,
December 10, 1998.
[15] G. J. Klinker, S. A. Shafer, and T. Kanade, "Physical Approach to Color
Image Understanding," Inter. J. of Computer Vision, Vol. 4, No.1, pp. 7-
38, January, 1990.
[16] Cagnoni, S., Dobrzeniecki, A.B., Poli, R. and Yanch, J.C., 1999,
"Genetic Algorithm-based Interactive Segmentation of 3D Medical
Images", Image and Vision Computing 17, pp. 881-895.
[17] Davis, L.S. and Rosenfeld, A., 1981, "Cooperating Processes for Low-
Level Vision:A Survey", Artificial Intelligence 17, pp.245-263.
[18] R.C.Dubes, A. K. Jain, S.G. Nadabar, and C. C. Chen, "MRF Model-
Based Algorithms for Image Segmentation," In: Proceedings of the 10th
ICPR Vol. 1, pp. 808-814, Atlantic City, NJ, USA, June 16-21, 1990.
[19] P. J. Besl and R. C. Jain, "Segmentation Through Variable-Order
Surface Fitting" IEEE Trans. on PAMI, Vol. 10, No. 2, pp.167-192,
March, 1988.
[20] M. Borsotti, P. Campadelli, and R. Schettini, "Quantitative Evaluation
of Color Image Segmentation Results," Pattern Recognition Letters,
Vol. 19, No. 8, pp. 741-747, June, 1998.
[1] R. M. Haralick and L. G. Shapiro, "Image Segmentation Techniques,"
CVGIP, Vol. 29, No. 1, pp. 100-132, January, 1985.
[2] N. R. Pal and S. K. Pal, "A Review on Image Segmentation Techniques,
" Pattern Recognition, Vol. 26, No. 9, pp. 1277-1294, September, 1993.
[3] M. Celenk, "A Color Clustering Technique for Image Segmentation,"
CVGIP, Vol. 52, No. 2, pp. 145-170, November, 1990.
[4] Bhandarkar, S.M., Zhang, Y. and Potter, W.D., 1994, "An Edge
Detection Technique using Genetic Algorithm-based Optimisation",
Pattern Recognition 27(9), pp. 1159-1180.
[5] B. Bhanu, S. Lee, and J. Ming, "Adaptive Image Segmentation Using a
Genetic Algorithm," IEEE Trans on Systems, Man and Cybernetics, Vol.
25, No. 12, pp. 15431567, December, 1995.
[6] D. N. Chun and H. S. Yang, "Robust Image Segmentation Using Genetic
Algorithm with a Fuzzy Measure," Pattern Recognition, Vol. 29, No. 7,
pp. 1195-1211, July, 1996.
[7] H. Derin and H. Elliott, "Modeling and Segmentation of Noisy and
Textured Images Using Gibbs Random Fields,"IEEE Trans. on PAMI,
Vol. 9, No. 1, pp. 39-55, January, 1987.
[8] J. Liu and Y. H. Yang, "Multi resolution Color Image Segmentation,"
IEEE Trans. on PAMI, Vol. 16, No. 7, pp. 689-700, July, 1994.
[9] S. H. Park, I. D. Yun, and S. U. Lee, "Color Image Segmentation Based
on 3-D Clustering," Pattern Recognition, Vol. 31, No. 8, pp. 1061-1076,
August, 1998.
[10] Andrey, P., 1999, "Selections Relaxation: Genetic Algorithms applied to
Image Segmentation", Image and Vision Computing 17, pp. 175-187.
[11] D. E. Goldberg, Genetic Algorithms in Search, Optimization, and
Machine Learning, Addison-Wesley, Reading, MA, USA, 1989.
[12] Chun, D.N. and Yang., H.S., 1996, "Robust Image Segmentation using
Genetic Algorithm with a Fuzzy Measure", Pattern Recognition 29(7),
pp. 1195-1211.
[13] S. M. Bhandarkar and H. Zhang, "Image Segmentation Using
Evolutionary Computation," IEEE Trans. on Evolutionary Comp., Vol.
3, No. 1, pp. 1-21, April, 1999.
[14] H. J. Kim, E. Y. Kim, J. W. Kim, and S. H. Park, "MRF Model Based
Image Segmentation Using Hierarchical Distributed Genetic
Algorithm," Electronics Letters, Vol. 34, No. 25, pp. 2394-2395,
December 10, 1998.
[15] G. J. Klinker, S. A. Shafer, and T. Kanade, "Physical Approach to Color
Image Understanding," Inter. J. of Computer Vision, Vol. 4, No.1, pp. 7-
38, January, 1990.
[16] Cagnoni, S., Dobrzeniecki, A.B., Poli, R. and Yanch, J.C., 1999,
"Genetic Algorithm-based Interactive Segmentation of 3D Medical
Images", Image and Vision Computing 17, pp. 881-895.
[17] Davis, L.S. and Rosenfeld, A., 1981, "Cooperating Processes for Low-
Level Vision:A Survey", Artificial Intelligence 17, pp.245-263.
[18] R.C.Dubes, A. K. Jain, S.G. Nadabar, and C. C. Chen, "MRF Model-
Based Algorithms for Image Segmentation," In: Proceedings of the 10th
ICPR Vol. 1, pp. 808-814, Atlantic City, NJ, USA, June 16-21, 1990.
[19] P. J. Besl and R. C. Jain, "Segmentation Through Variable-Order
Surface Fitting" IEEE Trans. on PAMI, Vol. 10, No. 2, pp.167-192,
March, 1988.
[20] M. Borsotti, P. Campadelli, and R. Schettini, "Quantitative Evaluation
of Color Image Segmentation Results," Pattern Recognition Letters,
Vol. 19, No. 8, pp. 741-747, June, 1998.
@article{"International Journal of Information, Control and Computer Sciences:60067", author = "Raghad Jawad Ahmed", title = "Genetic-Based Multi Resolution Noisy Color Image Segmentation", abstract = "Segmentation of a color image composed of different
kinds of regions can be a hard problem, namely to compute for an
exact texture fields. The decision of the optimum number of
segmentation areas in an image when it contains similar and/or un
stationary texture fields. A novel neighborhood-based segmentation
approach is proposed. A genetic algorithm is used in the proposed
segment-pass optimization process. In this pass, an energy function,
which is defined based on Markov Random Fields, is minimized. In
this paper we use an adaptive threshold estimation method for image
thresholding in the wavelet domain based on the generalized
Gaussian distribution (GGD) modeling of sub band coefficients. This
method called Normal Shrink is computationally more efficient and
adaptive because the parameters required for estimating the threshold
depend on sub band data energy that used in the pre-stage of
segmentation. A quad tree is employed to implement the multi
resolution framework, which enables the use of different strategies at
different resolution levels, and hence, the computation can be
accelerated. The experimental results using the proposed
segmentation approach are very encouraging.", keywords = "Color image segmentation, Genetic algorithm,Markov random field, Scale space filter.", volume = "4", number = "9", pages = "1442-7", }