Unsupervised image segmentation using automated fuzzy c-means

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dc.contributor.authorSahaphong, Supatra
dc.contributor.authorHiransakolwong, Nualsawat
dc.date.accessioned2026-08-06T09:56:12Z
dc.date.available2026-08-06T09:56:12Z
dc.date.issued2007-12-01
dc.description.abstractAn 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.
dc.identifier.citationCIT 2007 7th IEEE International Conference on Computer and Information Technology, 690-694, 2007
dc.identifier.doi10.1109/CIT.2007.4385165
dc.identifier.other2-s2.0-38049086148
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/1889
dc.sourceCIT 2007 7th IEEE International Conference on Computer and Information Technology
dc.subjectClustering
dc.subjectFuzzy c-means
dc.subjectImage segmentation
dc.subjectOtsu algorithm
dc.titleUnsupervised image segmentation using automated fuzzy c-means
dc.typeConference Paper

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