Abstract
Fuzzy clustering method for image segmentation usually needs the determination of the cluster number in advance. Therefore, an adaptive fuzzy clustering image segmentation algorithm based on jumping gene genetic algorithm (JGGA) is investigated in this paper. A new weighted multi-objective evaluation function considering the cluster number, the inner-class distance and the inter-class distance is proposed. Because the cluster number is uncertain during the optimization process, a variable-length JGGA (VJGGA) is introduced. The cluster number and the cluster centers of the image gray values are determined by the minimization of evaluation function. Simulation results of the segmentation for a real image indicate that VJGGA algorithm is characterized by strong global capability of searching the optimal segmentation number and cluster centers, compared with variable-length GA (VGA).
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References
Felzenszwalb, P., Huttenlocher, D.: Efficient Graph-Based Image Segmentation. International Journal of Computer Vision 59(2), 167–181 (2004)
Blake, A., Rother, C., Brown, M., Perez, P., Torr, P.: Interactive Image Segmentation Using an Adaptive GMMRF Model. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 428–441. Springer, Heidelberg (2004)
Awad, M.M., Chehdi, K., Nasri, A.: Multicomponent Image Segmentation: A Comparative Analysis Between a Hybrid Genetic Algorithm and Self-Organizing Maps. International Journal of Remote Sensing 30(3), 595–610 (2009)
Ning, J.F., Zhang, L., Zhang, D., Wu, C.K.: Interactive Image Segmentation by Maximal Similarity Based Regionmerging. Pattern Recognition 43, 445–456 (2010)
Widz, S., Slezak, D.: Approximation Degrees in Decision Reduct-Based MRI Segmentation. In: Frontiers in the Convergence of Bioscience and Information Technologies, Korea, October 11-13, pp. 431–436. IEEE Computer Society, Los Alamitos (2007)
Malyszko, D., Stepaniuk, J.: Adaptive Rough Entropy Clustering Algorithms in Image Segmentation. Fundamenta Informaticae 98, 199–231 (2010)
Zhen, Z.Y., Wang, Z.S., Liu, Y.Y.: Image Thresholding Method by Minimizing Fuzzy Entropy Function Based on Ant Colony Algorithm, Progress in Intelligence Computation and Applications. In: Second International Symposium on Intelligence Computation and Applications, September 21-23, pp. 308–311. China University of Geosciences Press, Wuhan (2007)
Zhen, Z.Y., Gu, Z., Liu, Y.Y.: A Novel Fuzzy Entropy Image Segmentation Approach Based on Grey Relational Analysis. In: Proceedings of 2007 IEEE International Conference on Grey Systems and Intelligent Services, Nanjing, China, November 18-20, pp. 1019–1022 (2007)
Liu, Y.Y., Liu, W.B., Zhen, Z.Y., Zhang, G.: Image Segmentation Based on Fuzzy Entropy and Grey Relational Analysis. In: Proceedings of the 4th International Conference on Image and Graphics, August 22-24, pp. 372–376. IEEE Computer Society Press, Los Alamitos (2007)
Liu, Y.Y., Liu, W.B., Zhen, Z.Y., Zhang, G.: Image Segmentation Method Based on Fuzzy Entropy and Grey Relational Analysis. Journal of Optoelectronics. Laser 19(9), 1250–1253 (2008)
Forouzanfar, M., Forghani, N., Teshnehlab, M.: Parameter Optimization of Improved Fuzzy C-Means Clustering Algorithm for Brain MR Image Segmentation. Engineering Applications of Artificial Intelligence 23, 160–168 (2010)
Zhen, Z.Y., Wang, D.B., Liu, W.B., Liu, Y.Y.: Variable-Length PSO Optimized Fuzzy Clustering for Self-Adaptive Image Segmentation. Journal of Optoelectronics. Laser 20(1), 99–102 (2009)
Chan, T.M., Man, K.F., Tang, K.S., Kwong, S.: A Jumping Gene Algorithm for Multi-Objective Resource Management in Wideband CDMA Systems. Computer Journal 48(6), 749–768 (2005)
Chan, T.M., Man, K.F., Tang, K.S., et al.: Multi-Objective Optimization of Radio-to-Fiber Repeater Placement Using a Jumping Gene Algorithm. In: IEEE International Conference on Industrial Technology, pp. 291–296 (2005)
Ripon, K.S.N., Kwong, S., Man, K.F.: A Real-Coding Jumping Gene Genetic Algorithm (RJGGA) for Multi-Objective Optimization. Information Science 177(2), 632–654 (2007)
Pu, H.Z., Zhen, Z.Y., Wang, D.B., Liu, Y.Y.: Improved Jumping Gene Genetic Algorithm for Multi-Peak Function Optimization. Nanjing University of Aeronautics and Astronautics 39(6), 829–832 (2007)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transaction on Evolutionary Computation 6(2), 182–197 (2002)
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Fang, Y., Zhen, Z., Huang, Z., Zhang, C. (2010). Multi-objective Fuzzy Clustering Method for Image Segmentation Based on Variable-Length Intelligent Optimization Algorithm. In: Cai, Z., Hu, C., Kang, Z., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2010. Lecture Notes in Computer Science, vol 6382. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16493-4_34
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DOI: https://doi.org/10.1007/978-3-642-16493-4_34
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