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Unsupervised image segmentation using automated fuzzy c-means
Author(s)
Sahaphong, Supatra
Hiransakolwong, Nualsawat
Date Issued
December 1, 2007
Type
Conference Paper
Abstract
An unsupervised fuzzy clustering technique, Fuzzy c-means (FCM) clustering algorithm has been widely used in image segmentation. However, the conventional FCM algorithm must be estimated by expertise users to determine the cluster numbers. To overcome the limitation of FCM algorithm, an automated fuzzy c-mean (AFCM) algorithm is presented in this paper. The proposed algorithm initiates the first two centroids of clusters by a method based on Otsu algorithm and automatically determines the appropriate cluster number for image segmentation. The performance of the proposed technique has been tested with reference to conventional FCM. The experimental results demonstrate that AFCM can spontaneously estimate the appropriate number of clusters and its performance is faster convergence than the performance of the conventional FCM. © 2007 IEEE.
Citation
CIT 2007 7th IEEE International Conference on Computer and Information Technology, 690-694, 2007