A new social and momentum component adaptive PSO algorithm for image segmentation |
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| Authors: | Akhilesh Chander Amitava Chatterjee Patrick Siarry |
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| Affiliation: | 1. Laboratoire Images, Signaux et Systèmes Intelligents (LiSSi, EA 3956), Université Paris XII Val de Marne, 61 avenue du Général de Gaulle, 94010 Créteil, France;2. Department of Electronics and Computers, Indian Institute of Technology, Roorkee 247667, India;3. Jadavpur University, Electrical Engineering Department, Kolkata 700 032, India;1. School of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore;2. School of Electrical and Electronics Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore;1. School of Mechanical Engineering, Hunan University of Science and Technology, Xiangtan, China;2. School of Resource Environment and Safety Engineering, Hunan University of Science and Technology, Xiangtan, China;3. Hunan Provincial Key Laboratory of Safe Mining Techniques of Coal Mines, Hunan University of Science and Technology, Xiangtan, China;1. Department of Computer Science and Engineering, C.V. Raman Global University, Bidya Nagar, Mahura, Janla, Bhubaneswar, Odisha 752054, India;2. Department of Computer Science and Information Technology, C.V. Raman Global University, Bidya Nagar, Mahura, Janla, Bhubaneswar, Odisha 752054, India;1. Tomas Bata University in Zlin, Faculty of Applied Informatics, Nam T.G. Masaryka 5555, 760 01 Zlin, Czech Republic;2. V?B–Technical University of Ostrava, Department of Computer Science, Faculty of Electrical Engineering and Computer Science, 17. listopadu 15, 708 33 Ostrava-Poruba, Czech Republic;1. Laboratoire LMIA and INRIA Grand-Est, University of Haute-Alsace, 4 rue des Frères Lumière, 68093 Mulhouse, France;2. Laboratoire LISSI, Université de Paris-Est Créteil, 61 Avenue du Général de Gaulle, 94010 Créteil, France;3. IRTES-SeT, Université de Technologie de Belfort-Montbéliard, 13 rue Thierry Mieg, 90000 Belfort, France |
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| Abstract: | In this paper, we present a new variant of Particle Swarm Optimization (PSO) for image segmentation using optimal multi-level thresholding. Some objective functions which are very efficient for bi-level thresholding purpose are not suitable for multi-level thresholding due to the exponential growth of computational complexity. The present paper also proposes an iterative scheme that is practically more suitable for obtaining initial values of candidate multilevel thresholds. This self iterative scheme is proposed to find the suitable number of thresholds that should be used to segment an image. This iterative scheme is based on the well known Otsu’s method, which shows a linear growth of computational complexity. The thresholds resulting from the iterative scheme are taken as initial thresholds and the particles are created randomly around these thresholds, for the proposed PSO variant. The proposed PSO algorithm makes a new contribution in adapting ‘social’ and ‘momentum’ components of the velocity equation for particle move updates. The proposed segmentation method is employed for four benchmark images and the performances obtained outperform results obtained with well known methods, like Gaussian-smoothing method (Lim, Y. K., & Lee, S. U. (1990). On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques. Pattern Recognition, 23, 935–952; Tsai, D. M. (1995). A fast thresholding selection procedure for multimodal and unimodal histograms. Pattern Recognition Letters, 16, 653–666), Symmetry-duality method (Yin, P. Y., & Chen, L. H. (1993). New method for multilevel thresholding using the symmetry and duality of the histogram. Journal of Electronics and Imaging, 2, 337–344), GA-based algorithm (Yin, P. -Y. (1999). A fast scheme for optimal thresholding using genetic algorithms. Signal Processing, 72, 85–95) and the basic PSO variant employing linearly decreasing inertia weight factor. |
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