|Paper title:||Image Thresholding Based on Bacterial Foraging and Pareto Multiobjective Optimization|
|Published in:||Issue 1, (Vol. 7) / 2013Download|
|Author(s):||DJEROU Leila, KHELIL Naceur , KHOMRI Bilal , BATOUCHE Mohamed|
|Abstract.||Social foraging behavior of Escherichia coli bacteria has recently been explored to develop a novel algorithm for distributed optimization and control. This paper exploits the metaphor of natural foraging of bacteria in the context of image segmentation. We adapt the bacteria chemotaxis multi-objective optimization algorithm to optimize simultaneously two segmentation criteria (Between-class variance criterion and entropy criterion) to improve the quality of the segmentation. The proposed method was evaluated on various types of images.The obtained results show the robustness of the method, and its non dependence towards the kind of the image to be segmented.|
|Keywords:||Bacterial Foraging, Image Segmentation, Image Thresholding, Multiobjective Optimization, Pareto Approach|
1. T.Abak, U.Baris, and B.Sankur, “The performance of thresholding algorithms for optical character recognition,” Proceedings of International Conference on Document Analytical Recognition, pp. 697–700, 1997.
2. M.Amos, D.Hodgson, A.Gibbons, “Bacterial self-organisation and computation,” International Journal of Unconventional Computing 3 (3), pp.199–210, 2007.
3. C.W. Bong and R. Mandava, “Multiobjective Optimization Approaches in Image Segmentation - The Directions and Challenges,” Int. J. Advance Soft Comput. Appl, Vol. 2, No. 1, pp. 40-64, 2010.
4. H.Chen, Y.Zhu, and K.Hu, “Multi-colony bacteria foraging optimization with cell-to-cell communication for RFID network planning,” Applied Soft Computing Journal, vol. 10, no. 2, pp. 539–547, 2010.
5. S.Das, A.Biswas, S.Dasgupta, and A.Abraham, “Bacterial foraging optimization algorithm: theoretical foundations, analysis, and applications,” Foundations of Computational Intelligence, Volume: 203, Publisher: Springer, pp. 23–55, 2009.
6. K.Deb, A.Pratap, S.Agarwal, and T.Meyarivan, “A fast and elitist multi-objective genetic algorithm: NSGA-II,” IEEE Transactions On Evolutionary Computation, 6 (2, 182–197), 2002.
7. L.De Castro and F.Von Zuben, “Recent Developments in Biologically Inspired Computing.” Idea Group Publishing, Hershey, USA, 2004.
8. M.Guzmán, A.Delgado, and J.D.Carvalho, “A novel multiobjective optimization algorithm based on bacterial chemotaxis,” Engineering Applications of Artificial Intelligence 23, pp.292–301, 2010.
9. K. Hammouche, M. Diaf and P. Siarry, “A comparative study of various meta-heuristic techniques applied to the multilevel thresholding problem,“ Journal of Engineering Applications of Artificial Intelligence, Vol. 23, 5, pp. 676-688, 2010.
10. Y.T.Hsiao, C.L.Chuang, Y.L.Lu, and J.A.Jiang, “Robust multiple objects tracking using image segmentation and trajectory estimation scheme in video frames,” Image Vision Comput. 24(10), pp.1123-1136, 2006.
11. J.N. Kapur, P.K. Sahoo and A.K.C. Wong, “A new method for gray-level picture thresholding using the entropy of the histogram,“ Journal of Computer Vision Graphics Image Processing, 29, pp. 273-285, 1985.
12. P.Kumar, S.Bandyopadhyay and S.Kumar, “Multi-objective Particle Swarm Optimization with time variant inertia and acceleration coefficients,” Information Sciences 177 (22), pp.5033–5049, 2007.
13. J.Lázaro, J.L.Martín, J.Arias, A.Astarloa and C.Cuadrado, “Neuro semantic thresholding using OCR software for high precision OCR applications,” Image Vision Comput. 28(4), pp. 571–578, 2010.
14. M.S.Li, W.J.Tang, W.H.Tang, Q.H.Wu, and J.R.Saunders, “Bacteria foraging algorithm with varying population for optimal power flow,” in Proc. Evol. Workshops LNCS vol. 4448. pp. 32-41, 2007.
15. A., Nakib, H. Oulhadj and P. Siarry, “Image thresholding based on Pareto multiobjective optimization,“ Engineering Applications of Artificial Intelligence, 23, pp. 313-320, 2010.
16. A., Nakib A. Nakib, H. Oulhadj and P. Siarry, “Image histogram thresholding based on multiobjective optimization,“ Signal Processing 87, pp. 2516-2534, 2007.
17. N. Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Trans. on Systems, Man, and Cybernetics, Vol. 9, 1, pp. 62-66, 1979.
18. N.R. Pal, and S.K. Pal, “A review on image segmentation techniques,” Pattern Recognition 9(26), pp. 1277-1994, 1993.
19. K.M.Passino, “Biomimicry of bacterial foraging for distributed optimization and control,” IEEE Control Systems Magazine, vol. 22, no. 3, pp. 52–67, 2002.
20. M. Reyes-Sierra, C.A.C. Coello, “Multi-objective Particle Swarm Optimizers: A survey of the State-of-the-Art,” Int. Journal of Comput. Intelligence Research, Vol. 2, 3, pp. 287- 308, 2006.
21. J.Robinson, and Y.Rahmat-Samii, “Particle swarm optimization in electro-magnetics,” IEEE Transactions on Antennas and Propagation, 52 (2), pp. 397–407, 2004.
22. P.K. Sahoo, S. Soltani, and A.K.C. Wong, “A survey of thresholding techniques,“, Computer Vision, Graphics, and Image Processing, Vol. 41, pp. 233-260, 1988.
23. M. Sezgin, B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation,” Journal of Electronic Imaging. Vol. 131, pp. 146-165, 2004.
24. M.Sezgin and R.Tasaltin, “A new dichotomization technique to multilevel thresholding devoted to inspection applications,” Pattern Recognition Lett. 21 (2), pp. 151–161, 2000.
25. W.J.Tang, Q.H.Wu, and J.R.Saunders, “A novel model for bacteria foraging in varying environments,” in Proc. ICCSA, LNCS vol. 3980., pp. 556-565, 2006.
26. R.H.Turi, Clustering-based color image segmentation. Ph.D Thesis. Monash University, Australia 2001.
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