A Quantum-Inspired Evolutionary Algorithm forMultiobjective Image Segmentation

Authors: Hichem Talbi, Mohamed Batouche, Amer Draa

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.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1334339

References:

[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.