Abstract
Fuzzy c-means clustering (FCM) with spatial constraints (FCM-S) is an effective algorithm suitable for image segmentation. Its effectiveness contributes not only to the introduction of fuzziness for belongingness of each pixel but also to exploitation of spatial contextual information. The contextual information can raise its insensitivity to noise to some extent. Although the robustness of the FCM-S algorithm is better, the convergence speed of it is lower. In this paper, to overcome the problem that FCM-S algorithm is time consuming, a fast fuzzy c-means clustering algorithm with spatial constraints (FFCM-S) is proposed. To speed up FCM-S calculations, FFCM-S algorithm modified the degree of memberships. Experiments on the artificial and real-world datasets show that our proposed algorithm is more effective.
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Li, Y., Li, G. (2010). Fast Fuzzy c-Means Clustering Algorithm with Spatial Constraints for Image Segmentation. In: Zeng, Z., Wang, J. (eds) Advances in Neural Network Research and Applications. Lecture Notes in Electrical Engineering, vol 67. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12990-2_49
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DOI: https://doi.org/10.1007/978-3-642-12990-2_49
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