Abstract
In this paper, we propose a new Chaotic MultiAgent System (CMAS) for image segmentation. This CMAS is a distributed system composed of a set of segmentation agents connected to a coordinator agent. Each segmentation agent performs Iterated Conditional Modes (ICM) starting from its own initial image created initially from the observed one by using a chaotic mapping. However, the coordinator agent receives and diversifies these images using a crossover and a chaotic mutation. A chaotic system is successfully used in order to benefit from the special chaotic characteristic features such as ergodic property, stochastic aspect and dependence on initialization. The efficiency of our approach is shown through experimental results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Andrey, P.: Selectionist Relaxation: Genetic Algorithms applied to Image Segmentation. Image and Vision Computing 17, 175–187 (1999)
Besag, J.: On the statistical analysis of dirty pictures. Jou. of the Royal Stat. Soc. 48, 259–302 (1986)
Bhanu, B., Lee, S.: Genetic Learning for Adaptive Image Segmentation. Kluwer Academic Publishers, Dordrecht (1994)
Bond, A.H., Gaser, L.: Readings in Distributed Artificial Intelligence. Morgan Kaufmann, San Mateo (1988)
Cagnoni, S., Dobrzeniecki, A.B., Poli, R., Yanch, J.C.: Genetic Algorithm-based Interactive Segmentation of 3D Medical Images. Image and Vision Computing 17, 881–895 (1999)
Chen, L.N., Aihara, K.: Chaotic simulated annealing by a neural network model with transient chaos. Neural Networks 8(6), 915–930 (1995)
Derin, H., Elliott, H.: Modeling and segmentation of noisy and textured images using Gibbs random fields. IEEE Trans. on PAMI 9, 39–55 (1987)
Dubes, R.C., Jain, A.K., Nadabar, S.G., Chen, C.C.: MRF model-based algorithms for image segmentation. IEEE Trans. on PAMI, 808–814 (1990)
Duchesnay, E., Montois, J.J., Jacquelet, Y.: Cooperative agents society organized as an irregular pyramid: a mammography segmentation application. In: Pattern Recognition Letters. Elsevier Science Inc., vol. 24(14), pp. 2435–2445. Elsevier Science Inc., Amsterdam (2003)
Ferber, J.: Multi-Agent System: An Introduction to Distributed Artificial Intelligence. Addison Wesley Longman, Harlow (1999)
Geman, S., Geman, D.: Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images. IEEE Trans. on PAMI 6(6), 721–741 (1984)
Goldberg, D.E.: Genetic algorithm in search, optimization and machine learning, pp. 28–60. Addison-Wesley, Reading (1989)
Haralick, R.M., Shapiro, L.G.: Survey: Image Segmentation. Computer Vision, Graphics and Image Processing 29, 100–132 (1985)
Holland, J.H.: Adaptation in natural and artificial system. The University of Michigan press, Ann Arbor (1975)
Kato, Z., Zerubia, T., Berthod, M.: Satellite image classification using a Metropolis dynamics. In: Proc. of ICQSSP, San Francisco, vol. 3, pp. 573–576 (1992)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220(4), 671–680 (1983)
Koza, J.R.: Survey of genetic algorithms and genetic programming. In: Proc. of the Wescon95-Conf. Record: Microelectronics, Communications Technology, Producing Quality Products, Mobile and Portable Power, Emerging Technologies, San Francisco, CA, USA (1995)
Lakshmanan, S., Derin, H.: Simultaneous Parameter Estimation and Segmentation of Gibbs Random Fields Using Simulated Annealing. IEEE Trans. on PAMI 11, 799–813 (1989)
Florea, A.M.: Introduction to Multi-Agent Systems. In: International Summer School on Multi-Agent Systems, Bucharest (1998)
Kicinger, R., Arciszewski, T., De Jong, K.: Distributed Evolutionary Design: Island-Model Based Optimization of Steel Skeleton Structures in Tall Buildings. In: IEEE CEC 2004, Portland, USA (2004)
Lee, P.-H., Pei, S.-C., Chen, Y.-Y.: Generating Chaotic Stream Ciphers Using Chaotic Systems. Chinese Journal of physics 41(6) (2003)
Lorenz, E.N.: Deterministic non-periodic flow. J. Atmos. Sci. 20, 130 (1963)
Mingjun, J., Huanwen, T.: Application of Chaos Simulated Annealing. Chaos, Solitons and Fractals 21, 933–941 (2004)
Richard, N., Dojat, M., Garbay, C.: Multi-agent Approach for Image Processing: A Case Study for MRI Human Brain Scans Interpretation. In: Dojat, M., Keravnou, E.T., Barahona, P. (eds.) AIME 2003. LNCS (LNAI), vol. 2780, pp. 91–100. Springer, Heidelberg (2003)
Yang, L., Chen, T.: Application chaos in genetic algorithms. Commun. Theor Phys. 38, 168–172 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Melkemi, K.E., Batouche, M., Foufou, S. (2006). MRF Model-Based Approach for Image Segmentation Using a Chaotic MultiAgent System. In: Bloch, I., Petrosino, A., Tettamanzi, A.G.B. (eds) Fuzzy Logic and Applications. WILF 2005. Lecture Notes in Computer Science(), vol 3849. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11676935_43
Download citation
DOI: https://doi.org/10.1007/11676935_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32529-1
Online ISBN: 978-3-540-32530-7
eBook Packages: Computer ScienceComputer Science (R0)