A Quantum-Inspired Evolutionary Algorithm forMultiobjective Image Segmentation
In this paper we present a new approach to deal with
image segmentation. The fact that a single segmentation result do not
generally allow a higher level process to take into account all the
elements included in the image has motivated the consideration of
image segmentation as a multiobjective optimization problem. The
proposed algorithm adopts a split/merge strategy that uses the result
of the k-means algorithm as input for a quantum evolutionary
algorithm to establish a set of non-dominated solutions. The
evaluation is made simultaneously according to two distinct features:
intra-region homogeneity and inter-region heterogeneity. The
experimentation of the new approach on natural images has proved
its efficiency and usefulness.
[1] R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis,
John Wiley & Sons, New-York, 1973.
[2] K. S. Fu and J. K. Mei, "A survey on image segmentation," Pattern
Recognifion, vol. 13, pp. 3-16, 1981.
[3] R. Pal and S. K. Pal, "A review in image segmentation techniques,"
Pattern Recognition, vol. 26, pp. 1277-1294, 1993.
[4] S. Y. Ho and K. Z. Lee, "An efficient evolutionary image segmentation
algorithm,", in Proc. IEEE Congress on Evolutionary Computation, pp.
1327-1334, 2001.
[5] T. N. Pappas, "An adaptive clustering algorithm for image
segmentation," IEEE Trans. on Signal Processing, vol. 40, no. 4, pp.
901-914, 1992.
[6] J. T. Tou and R. C. Gonzalez, Pattern Recognition Principles. Reading,
MA: Addison-Wesley, 1974.
[7] P. K. Sahoo, S. Soltani, and A. K. C. Wong, "A survey of thresholding
technique," CVGIP 41, pp. 233-260, 1988.
[8] J. D. Helterbrand, "One-pixel-wide closed boundary identification,"
IEEE Trans. on Image Processing, vol. 5, no. 5, pp.780-783, 1996.
[9] Y. L. Chang and X. Li, "Adaptive image region-growing," IEEE Trans.
on Image Processing, vol. 3, no. 6, pp. 868-872, 1994.
[10] R. Adams and L. Bischof, "Seeded region growing," IEEE Trans. on
Pattern Analysis and Machine Intelligence, vol. 16, no. 6, pp. 641-647,
1994.
[11] S. A. Hojjatoleslami and J. Kittler, "Region growing: a new approach,"
IEEE Trans. on Image Processing, vol. 7, no. 7, pp. 1079-1084, 1998.
[12] M. R. Rezaee, P. M. J. van der Zwet, B. P. E Lelieveldt, R. J. van der
Geest, and J. H. C. Reiber, "A multiresolution image segmentation
technique based on pyramidal segmentation and fuzzy clustering," IEEE
Trans. on Image Processing, vol. 9, no. 7, pp. 1238-1248, 2000.
[13] 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, 1996.
[14] S. M. Bhandarkar and H. Zhang, "Image segmentation using
evolutionary computation," IEEE Trans. On Evolutionary Computation,
vol. 3, no. 1, pp. 1-21, 1999.
[15] D. Deutsch, "Quantum theory, the Church-Turing principle and the
universal quantum computer," in Proc. Royal Society of London, A 400,
pp. 97-117, 1985.
[16] P. Shor, "Algorithms for quantum computation: discrete logarithms and
factoring," in Proc., 35th Annual Symposium on Foundations of
Computer Science, IEEE Press, Nov. 1994.
[17] E. Rieffel and W. Polak, "An introduction to quantum computing for
non-physicists," arxive.org, quant-ph/9809016 v2, Jan. 2000.
[18] K. Han and J. Kim, "Quantum-inspired evolutionary algorithm for a
class of combinatorial optimization," IEEE Trans. On Evolutionary
Computation, vol. 6, no. 6, Dec. 2002.
[19] Y. Kim, J. Kim, K. Han, "Quantum-inspired multiobjective evolutionary
algorithm for multiobjective 0/1 knapsack problems," in Proc. IEEE
Congress on Evolutionary Computation, pp. 9151-9156, Jul. 2006.
[20] H. Talbi, A. Draa, and M. Batouche, "A new quantum-inspired genetic
algorithm for solving the travelling slesman problem," in Proc. IEEE
International Conference on Industrial Technology, Vol. 3, pp.1192 -
1197, Dec. 2004.
[21] A. Draa, H. Talbi, and M. Batouche, "A quantum-inspired genetic
algorithm for solving the n-queens problem," in Proc. 7th International
Symposium on Programming and Systems, pp.145-152, May 2005.
[22] A. Draa, M. Batouche, and H. Talbi, "A quantum-inspired differential
evolution algorithm for rigid image registration," ìn Proc. International
Conference on Computational Intelligence, pp.408-41, Dec. 2004.
[23] H. Talbi, A. Draa, and M. Batouche, "A Quantum-Inspired Evolutionary
Algorithm for Multi-Sensor Image Registration," International Arabic
Journal on Information Technology, Vol. 3, No 1, pp. 9-15, Jan. 2006.
[24] C. Coello, "A comparative survey of evolutionary-based multiobjective
optimization techniques," Knowledge and Information Systems 1, pp.
269-308, 1999.
[25] C. Coello, "A comprehensive survey of evolutionary-based
multiobjective optimization techniques," Knowledge and Information
Systems. vol. 1, no. 3, pp. 269-308, 1999.
[1] R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis,
John Wiley & Sons, New-York, 1973.
[2] K. S. Fu and J. K. Mei, "A survey on image segmentation," Pattern
Recognifion, vol. 13, pp. 3-16, 1981.
[3] R. Pal and S. K. Pal, "A review in image segmentation techniques,"
Pattern Recognition, vol. 26, pp. 1277-1294, 1993.
[4] S. Y. Ho and K. Z. Lee, "An efficient evolutionary image segmentation
algorithm,", in Proc. IEEE Congress on Evolutionary Computation, pp.
1327-1334, 2001.
[5] T. N. Pappas, "An adaptive clustering algorithm for image
segmentation," IEEE Trans. on Signal Processing, vol. 40, no. 4, pp.
901-914, 1992.
[6] J. T. Tou and R. C. Gonzalez, Pattern Recognition Principles. Reading,
MA: Addison-Wesley, 1974.
[7] P. K. Sahoo, S. Soltani, and A. K. C. Wong, "A survey of thresholding
technique," CVGIP 41, pp. 233-260, 1988.
[8] J. D. Helterbrand, "One-pixel-wide closed boundary identification,"
IEEE Trans. on Image Processing, vol. 5, no. 5, pp.780-783, 1996.
[9] Y. L. Chang and X. Li, "Adaptive image region-growing," IEEE Trans.
on Image Processing, vol. 3, no. 6, pp. 868-872, 1994.
[10] R. Adams and L. Bischof, "Seeded region growing," IEEE Trans. on
Pattern Analysis and Machine Intelligence, vol. 16, no. 6, pp. 641-647,
1994.
[11] S. A. Hojjatoleslami and J. Kittler, "Region growing: a new approach,"
IEEE Trans. on Image Processing, vol. 7, no. 7, pp. 1079-1084, 1998.
[12] M. R. Rezaee, P. M. J. van der Zwet, B. P. E Lelieveldt, R. J. van der
Geest, and J. H. C. Reiber, "A multiresolution image segmentation
technique based on pyramidal segmentation and fuzzy clustering," IEEE
Trans. on Image Processing, vol. 9, no. 7, pp. 1238-1248, 2000.
[13] 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, 1996.
[14] S. M. Bhandarkar and H. Zhang, "Image segmentation using
evolutionary computation," IEEE Trans. On Evolutionary Computation,
vol. 3, no. 1, pp. 1-21, 1999.
[15] D. Deutsch, "Quantum theory, the Church-Turing principle and the
universal quantum computer," in Proc. Royal Society of London, A 400,
pp. 97-117, 1985.
[16] P. Shor, "Algorithms for quantum computation: discrete logarithms and
factoring," in Proc., 35th Annual Symposium on Foundations of
Computer Science, IEEE Press, Nov. 1994.
[17] E. Rieffel and W. Polak, "An introduction to quantum computing for
non-physicists," arxive.org, quant-ph/9809016 v2, Jan. 2000.
[18] K. Han and J. Kim, "Quantum-inspired evolutionary algorithm for a
class of combinatorial optimization," IEEE Trans. On Evolutionary
Computation, vol. 6, no. 6, Dec. 2002.
[19] Y. Kim, J. Kim, K. Han, "Quantum-inspired multiobjective evolutionary
algorithm for multiobjective 0/1 knapsack problems," in Proc. IEEE
Congress on Evolutionary Computation, pp. 9151-9156, Jul. 2006.
[20] H. Talbi, A. Draa, and M. Batouche, "A new quantum-inspired genetic
algorithm for solving the travelling slesman problem," in Proc. IEEE
International Conference on Industrial Technology, Vol. 3, pp.1192 -
1197, Dec. 2004.
[21] A. Draa, H. Talbi, and M. Batouche, "A quantum-inspired genetic
algorithm for solving the n-queens problem," in Proc. 7th International
Symposium on Programming and Systems, pp.145-152, May 2005.
[22] A. Draa, M. Batouche, and H. Talbi, "A quantum-inspired differential
evolution algorithm for rigid image registration," ìn Proc. International
Conference on Computational Intelligence, pp.408-41, Dec. 2004.
[23] H. Talbi, A. Draa, and M. Batouche, "A Quantum-Inspired Evolutionary
Algorithm for Multi-Sensor Image Registration," International Arabic
Journal on Information Technology, Vol. 3, No 1, pp. 9-15, Jan. 2006.
[24] C. Coello, "A comparative survey of evolutionary-based multiobjective
optimization techniques," Knowledge and Information Systems 1, pp.
269-308, 1999.
[25] C. Coello, "A comprehensive survey of evolutionary-based
multiobjective optimization techniques," Knowledge and Information
Systems. vol. 1, no. 3, pp. 269-308, 1999.
@article{"International Journal of Information, Control and Computer Sciences:56820", author = "Hichem Talbi and Mohamed Batouche and Amer Draa", title = "A Quantum-Inspired Evolutionary Algorithm forMultiobjective Image Segmentation", abstract = "In this paper we present a new approach to deal with
image segmentation. The fact that a single segmentation result do not
generally allow a higher level process to take into account all the
elements included in the image has motivated the consideration of
image segmentation as a multiobjective optimization problem. The
proposed algorithm adopts a split/merge strategy that uses the result
of the k-means algorithm as input for a quantum evolutionary
algorithm to establish a set of non-dominated solutions. The
evaluation is made simultaneously according to two distinct features:
intra-region homogeneity and inter-region heterogeneity. The
experimentation of the new approach on natural images has proved
its efficiency and usefulness.", keywords = "Image segmentation, multiobjective optimization,quantum computing, evolutionary algorithms.", volume = "1", number = "7", pages = "2069-3", }
