International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 4, No. 12, 8 Nov. 2012
Cover page and Table of Contents: PDF (size: 547KB)
Handling Fuzzy Image Clustering with a Modified ABC Algorithm
Full Text (PDF, 547KB), PP.65-74
Views: 0 Downloads: 0
Author(s)
Salima Ouadfel
1,*
Souham Meshoul
1
Index Terms
Image Segmentation, Fuzzy Clustering, Optimization, Artificial Bees Colony
Abstract
Image segmentation can be cast as a clustering task where the image is partitioned into clusters. Pixels within the same cluster are as homogenous as possible whereas pixels belonging to different clusters are not similar in terms of an appropriate similarity measure. Several clustering methods have been proposed for image segmentation purpose among which the Fuzzy C-Means clustering algorithm. However this algorithm still suffers from some drawbacks, such as local optima and sensitivity to initialization. Artificial Bees Colony algorithm is a recent population-based optimization method which has been successfully used in many complex problems. In this paper, we propose a new fuzzy clustering algorithm based on a modified Artificial Bees Colony algorithm, in which a new mutation strategy inspired from the Differential Evolution is introduced in order to improve the exploitation process. Experimental results show that our proposed approach improves the performance of the basic fuzzy C-Means clustering algorithm and outperforms other population based optimization methods.
Cite This Paper
Salima Ouadfel, Souham Meshoul, "Handling Fuzzy Image Clustering with a Modified ABC Algorithm", International Journal of Intelligent Systems and Applications(IJISA), vol.4, no.12, pp.65-74, 2012. DOI:10.5815/ijisa.2012.12.09
Reference
[1]H. H Mohammed, Unsupervised fuzzy clustering and image segmentation using weighted neural networks”, CIAP03308-313. 2003
[2]N.R Pal, and S.K Pal, A review on image segmentation techniques”, Pattern Recognition 926: 1277–1294, 1993.
[3]L.A. Zadeh, Fuzzy sets, Inform. Control 8 338–353. 1965
[4]D.W. Kim, K.H. Lee, and D. Lee, A novel initialization scheme for the fuzzy c-means algorithm for color clustering, Pattern Recognition letters 25, No. 2, , pp. 227-237. January 2004
[5]S. Liew, Leung and W. Lau. Fuzzy Image Clustering Incorporating Spatial Continuity. In IEE Proceedings Vision, Image and Signal Processing, vol. 147, no. 2, 2000.
[6]Y. Marlnakls, M. Marmakl, and N. Matsatsinls, A Hybrid Discrete Artificial Bee Colony – GRASP Algorithm for Clustering. Decision Support Systems, 548-553 2009.. IEEE.
[7]J. G. Klir and B. Yuan, Fuzzy sets and fuzzy logic, theory and applications, Prentice-Hall Co., 2003.
[8]D.E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning..Addison-Wesley, Reading, MA 1989.
[9]M. Dorigo,, Stutzle,. The ant colony optimization metaheuristic: algorithms, applications and advances. Technical Report IRIDIA-32 2000.
[10]J. Kennedy and R.C Eberhart, Particle swarm optimization. In Proc. IEEE Int. Conf. Neural Netw., Perth, Australia,. vol. 4, 1948–1972. 1995
[11]R. Storn, and K. Price, Differential Evolution — a simple and efficient heuristic for global optimization over continuous spaces. Technical Report TR-95-012.,ICSI. 1995
[12]D. Karaboga, An idea based on honey bee swarm for numerical optimization, technical Report TR06, Erciyes University, Engineering Faculty, Computer Engineering Department,. 2005
[13]F. Klawonn, and A. Keller. Fuzzy clustering with evolutionary algorithms. International Journal of Intelligent Systems, , 13: 975–991. 1998
[14]J. Bezdek, and R. Hathaway. Optimization of fuzzy clustering criteria using genetic algorithms. in: Proc. of the IEEE Conf. on Evolutionary Computation, vol. 2, , 589–594. 1994
[15]B. Zhao, An Ant Colony Clustering Algorithm, Sixth International Conference on Machine Learning and Cybernetics, Hong. Kong.. pp.3933-3938 2007.
[16]W. Liu, J. L. A clustering algorithm FCM-ACO for supplier base management. Lecture Notes in Computer Science including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics,. 6440 LNAIPART 1, 106-113. 2010
[17]A. Szabo, L.N.D. Castro, and M.R. Delgado, The proposal of a fuzzy clustering algorithm based on particle swarm. 2011 Third World Congress on Nature and Biologically Inspired Computing. IEEE.
[18]C. Li, J. Zhou, P. Kou, and J. Xiao, A novel chaotic particle swarm optimization based fuzzy clustering algorithm. Neurocomputing, 83, 98-109. 2012. Elsevier.
[19]E. Sivaraman, S. Arulselvi, and K. Babu, Data driven fuzzy c-means clustering based on particle swarm optimization for pH process. 2011 International Conference on Emerging Trends in Electrical and Computer Technology. IEEE 2011.
[20]F. Samadzadegan, and A.A. Naeini,. Fuzzy clustering of hyperspectral data based on particle swarm optimization. 2011 3rd Workshop on Hyperspectral Image and Signal Processing Evolution in Remote Sensing WHISPERS. 2011IEEE.
[21]T. Runkler, and C. Katz. Fuzzy clustering by particle swarm optimization. IEEE Int. Conf. on Fuzzy Systems, 601–608. 2006
[22]S. Das, and S. Sil, Kernel-induced fuzzy clustering of image pixels with an improved differential evolution algorithm. Information Sciences, . 1808, 1237-1256. 2010 Elsevier Inc.
[23]S. Das, A. Abraham, and A. Konar, Automatic Clustering Using an Improved Differential Evolution Algorithm. IEEE Transactions on Systems Man and Cybernetics Part A Systems and Humans. IEEE. 2008.
[24]U. Maulik, and I. Saha, Automatic Fuzzy Clustering Using Modified Differential Evolution for Image Classification. IEEE Transactions on Geoscience and Remote Sensing. 2010.
[25]X. Zhang, Q. Bai, Q., and X. Yun, A combinatorial Artificial Bee Colony algorithm for travelling salesman problem. IEEE 3rd International Conference on Communication Software and Networks. 2011. IEEE.
[26]Z. Chi, J. Yan, and T. Pham, Fuzzy algorithms with application to image processing and pattern recognition”. World Scientific, Singopore 1996.
[27]C. Zhang, D. Ouyang, and J. Ning, An artificial bee colony approach for clustering. Expert Systems with Applications, 377, 4761-4767. 2011 Elsevier Ltd.
[28]M. Taherdangkoo, M. Yazdi, and M.H. Rezvani, Segmentation of MR brain images using FCM improved by artificial bee colony ABC algorithm. Proceedings of the 10th IEEE International Conference on Information Technology and Applications in Biomedicine. IEEE 2010..
[29]A. Singh, An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem, Applied Soft Computing 9 2 625–631 2009.
[30]D. Karaboga, B. Gorkemli, C. Ozturk, and N. Karaboga, A comprehensive survey: artificial bee colony ABC algorithm and applications, Artificial Intelligence Review 2012.
[31]A. Abraham, R. Kumar, and A. Rajasekhar, Hybrid Differential Artificial Bee Colony Algorithm, Journal of Computational and Theoretical Nanoscience, USA, Volume 9, Number 2, pp. 249-257, 2012.
[32]B. Akay, and D. Karaboga, A modified artificial bee colony algorithm for real-parameter optimization, Information Sciences, 2011, doi:10.1016/j.ins.2010.07.015.
[33]B. Alatas, Chaotic bee colony algorithms for global numerical optimization. Expert Systems with Applications, 378, 5682-5687 2010. Elsevier Ltd.
[34]D. Zhang, X. Guan, Y. Tang, and Y. Tang, Modified Artificial Bee Colony Algorithms for Numerical Optimization. 3rd International Workshop on Intelligent Systems and Applications. IEEE. 2011.
[35]H.B. Duan, C.F. Xu, and Z.H. Xing, A hybrid artificial bee colony optimization and quantum evolutionary algorithm for continuous optimization problems International Journal of Neural Systems, 20 1, pp. 39–50 2010
[36]I.Jr. Fister, I. Fister and J. Brest. A Hybrid Artificial Bee Colony Algorithm for Graph 3-Coloring. In Swarm and Evolutionary Computation, Lecture Notes in Computer Science,7269, Springer Berlin / Heidelberg, 66 74 2012.
[37]J. Li, Q. Pan, and S. Xie A hybrid artificial bee colony algorithm for flexible job shop scheduling problems International Journal of Computers Communications & Control, 6 2, pp. 286–296 2011
[38]K.R. Gandhi, S.M. Uma and M. Karnan A Hybrid Meta Heuristic Algorithm for Discovering Classification Rule in Data Mining, IJCSNS International Journal of Computer Science and Network Security, VOL.12 No.4, April 2012
[39]L. Xiujuan, H. Xu, and Z. Aidong, Improved artificial bee colony algorithm and its application in data clustering. 2010 IEEE Fifth International Conference on BioInspired Computing Theories and Applications BICTA pp. 514-521. IEEE. 2012
[40]N. Baktash and M. R. Meybodi A New Hybrid Model of PSO and ABC Algorithms for Optimization in Dynamic Environment. International Journal of Computer Theory and Engineering, Vol. 4, No. 3, June 2012
[41]W. Gao, and S. Liu, A modified artificial bee colony algorithm. Computers & Operations Research, 393, 687-697 2012.. Elsevier.
[42]W. Zou, Y. Zhu, H. Chen, and X. Sui, A clustering approach using cooperative artificial bee colony algorithm, Discrete Dynamics in Nature and Society, vol. 2010, Article ID 459796, 16 pages, 2010.
[43]K, Price, R.M. Storn, and J.A. Lampinen, Differential Evolution: A Practical Approach to Global Optimization. Springer. ISBN 978-3-540-20950-8. 2005
[44]J. C Bezdek. Cluster validity with fuzzy sets. J Cybern;3:58–73. 1974
[45]L.O. Hall, and P.M. Kanade, Swarm Based Fuzzy Clustering with Partition Validity. The 14th IEEE International Conference on Fuzzy Systems 2005 FUZZ 05. Ieee. 2005
[46]X. L. Xie and G. Beni, A validity measure for fuzzy clustering. IEEE Trans. Pattern Anal. Mach. Intell., vol. 13, no. 8, pp. 841–847, Aug. 1991.