Abstract
Multi-threshold segmentation is a basic and widely used technique in image segmentation. The key step of accomplishing this task is to find the optimal multi-threshold value, which in essence can be reduced to multi-objective optimization problem. The quantum particle-behaved swarm algorithm (QPSO) is an effective method to resolve the problem of this class. However in practice, we found the original QPSO has imperfections, such as the excessive dropping of the diversity of the population and trapping in local optimum. In order to improve the ability of searching the global optimum and accelerate the speed of convergence, we proposed an improved quantum-behaved particle swarm algorithm (IQPSO). The experiments showed that IQPSO was superior to PSO and QPSO on the searching of multi-threshold value in image segmentation under the premise of ensuring the accuracy of solutions.
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Xiping, L., Jiei, T.: A survey of image segmentation. Pattern Recogn. Artif. Intell. 12(3), 300–312 (1999)
Pal, N.R., Pal, S.K.: A review on image segmentation techniques. Pattern Recogn. 26(9), 1277–1294 (1993)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)
Kapur, J.N., Sahoo, P.K., Wong, A.K.C.: A new method for gray-level picture thresholding using the entropy of the histogram. Comput. Vision Graph. Image Process. 29(3), 273–285 (1985)
Kittler, J., Illingworth, J.: Minimum error thresholding. Pattern Recogn. 19(1), 41–47 (1986)
Sun, J., Feng, B., Xu, W.-B.: Particle swarm optimization with particles having quantum behavior. In: Proceedings of 2004 Congress on Evolutionary Computation, pp. 325–331. Piscataway, NJ (2004)
Sun, J., Xu, W.-B., Feng, B.: A global search strategy of quantum-behaved particle swarm optimization. In: Proceedings of 2004 IEEE Conference on Cybernetics and Intelligent Systems, pp. 111–115. Singapore (2004)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks (1995)
Sun, J., Wu, X.J., Palade, V., Fang, W., Lai, C.-H., Xu, W.: Convergence analysis and improvements of quantum-behaved particle swarm optimization. Inf. Sci. 193, 81–103 (2012)
Sun, J., Xu, W.-B., Liu, J.: Parameter selection of quantum-behaved particle swarm optimization. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3612, pp. 543–552. Springer, Heidelberg (2005)
Cheng, W., Chen, S.F.: QPSO with self-adapting adjustment of inertia weight. Comput. Eng. Appl. 46(9), 46–48 (2010)
Gong, S.-F., Gong, X.-Y., Bi, X.-R.: Feature selection method for network intrusion based on GQPSO attribute reduction. In: International Conference on Multimedia Technology, pp. 6365–6358 (26–28 July 2011)
Gao, H., Xu, W.B., Gao, T.: A cooperative approach to quantum-behaved particle swarm optimization. In: Proceedings of IEEE International Symposium on Intelligent Signal Processing, IEEE, Alcala de Henares (2007)
Lu, S.F., Sun, C.F.: Co evolutionary quantum-behaved particle swarm optimization with hybrid cooperative search. In: Proceedings of Pacific-Asia Workshop on Computational Intelligence and Industrial Application, IEEE, Wuhan (2008)
Lu, S.F., Sun, C.F.: Quantum-behaved particle swarm optimization with cooperative-competitive co evolutionary. In: Proceedings of International Symposium on Knowledge Acquisition and Modeling, IEEE, Wuhan (2008)
Clerk, M., Kennedy, J.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6(1), 58–73 (2002)
Sun, J., Xu, W.B., Feng, B.: Adaptive parameter control for quantum-behaved particle swarm optimization on individual level. In: Proceedings of 2005 IEEE International Conference on Systems, Man and Cybernetics, pp. 3049–3054. Piscataway (2005)
Brest, J., Greiner, S., Boskovic, B., et al.: Self adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans. Evol. Comput. 10(6), 646–657 (2006)
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This work is supported by the open fund of the key laboratory in Southeast University of computer network and information integration of the ministry of education (Grant No. K93-9-2015-10C).
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Jiali, W., Hongshen, L., Yue, R. (2015). Multi-threshold Image Segmentation Through an Improved Quantum-Behaved Particle Swarm Optimization Algorithm. In: Huang, Z., Sun, X., Luo, J., Wang, J. (eds) Cloud Computing and Security. ICCCS 2015. Lecture Notes in Computer Science(), vol 9483. Springer, Cham. https://doi.org/10.1007/978-3-319-27051-7_10
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DOI: https://doi.org/10.1007/978-3-319-27051-7_10
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