Abstract
This paper presents a novel optimal multilevel thresholding algorithm for histogram-based image segmentation. The proposed algorithm presents an improved variant of the gravitational search algorithm (GSA), a relatively recently introduced stochastic optimization strategy. To strengthen its ability to achieve generation jumping when getting stuck at local optima, this paper proposes a novel algorithm, GA-GSA (genetic algorithm-based gravitational search algorithm) for image segmentation. In this paper, the proposed method employs both GA and GSA and the maximum entropy criterion as the objective function for achieve multilevel thresholding. To demonstrate the ability of the proposed algorithm, the novel method is employed on two benchmark images, and the performances obtained outperform results obtained using two other stochastic optimization methods, i.e., PSO (Particle Swarm Optimization) and GSA. The experimental results illustrate that the proposed algorithm could significantly enhance performance compared to other popular contemporary methods.
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Zhang, Y., Huang, D.: Image Segmentation Using PSO and PCM with Mahalanobis Distance. Expert Systems with Applications 38, 9036–9040 (2011) (in Chinese)
Otsu, N.: A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernet SMC-9, 62–66 (1979)
Lim, Y.K., Lee, S.U.: On the Color Image Segmentation Algorithm Based on the Thresholding and the Fuzzy C-Means Techniques. Pattern Recognition 23, 935–952 (1990)
Holland, J.H.: Adaptation in Nature and Artificial Systems. The University of Michigan Press, USA (1975)
Rashedi, E., Nezamabadi-pour, H.: GSA: A Gravitational Search Algorithm. Information Sciences 179, 2232–2248 (2009)
Shaw, B., Mukherjee, V.: A Novel Opposition-Based Gravitational Search Algorithm for Com-bined Economic and Emission Dispatch Problems of Power Systems. International Journal of Electrical Power and Energy Systems 35, 21–33 (2012)
Juang, C.F.: A Hybrid of Genetic Algorithm and Particle Swarm Optimization for Recurrent Network Design. IEEE Transactions on Systems, Man, and Cybernetics, Part B 34, 997–1006 (2004)
Mohsen, F.M.A., Hadhoud, M.M.: A New Optimization-Based Image Segmentation Method by Par-ticle Swarm Optimization. International Journal of Advanced Computer Science and Applications, 10–18 (2011)
Sahoo, P.K., Soltani, S.: A Survey of Thresholding Techniques. Computer Vision Graphics Image Processing 41, 233–260 (1988)
Yin, P.Y.: A Fast Scheme for Optimal Thresholding Using Genetic Algorithms. Signal Processing 72, 85–95 (1999)
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Sun, G., Zhang, A. (2013). A Hybrid Genetic Algorithm and Gravitational Search Algorithm for Image Segmentation Using Multilevel Thresholding. In: Sanches, J.M., Micó, L., Cardoso, J.S. (eds) Pattern Recognition and Image Analysis. IbPRIA 2013. Lecture Notes in Computer Science, vol 7887. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38628-2_84
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DOI: https://doi.org/10.1007/978-3-642-38628-2_84
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38627-5
Online ISBN: 978-3-642-38628-2
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