Abstract
Fuzzy C-means algorithms (FCMs) incorporating local information has been widely used for image segmentation, especially on image corrupted by noise. However, they cannot obtain the satisfying segmentation performance on the image heavily contaminated by noise, sensitivity to initial points, and can be trapped into local optima. Hence, optimization techniques are often used in conjunction with algorithms to improve the performance. In this paper, Particle Swarm Optimization (PSO) is introduced into fast generalized FCM (FGFCM) incorporating with local spatial and gray information called PFGFCM, where the membership degree values were modified by applying optimal-selection-based suppressed strategy. Experimental results on synthetic and real images heavily corrupted by noise show that the proposed method is superior to other fuzzy algorithms.
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Tran, D.C., Wu, Z., Tran, V.H. (2014). Fast Generalized Fuzzy C-means Using Particle Swarm Optimization for Image Segmentation. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8835. Springer, Cham. https://doi.org/10.1007/978-3-319-12640-1_32
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DOI: https://doi.org/10.1007/978-3-319-12640-1_32
Publisher Name: Springer, Cham
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