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Stable and unsupervised fuzzy C-means method and its validation in the context of multicomponent images

Abstract : A stable and unsupervised version of the fuzzy C-means algorithm, named FCM-optimized (FCMO), is presented. The originality of the proposed algorithm stems from (1) the introduction of an adaptive incremental procedure to initialize class centers, which makes the algorithm stable and deterministic; therefore, the classification results do not vary from one run to another and (2) the use of an unsupervised evaluation criterion to estimate the optimal number of classes. The validation of FCMO with regard to stability, reliability in class number estimation, and classification efficiency is shown through experimental results on synthetic monocomponent and real multicomponent images
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https://hal-univ-rennes1.archives-ouvertes.fr/hal-01300888
Contributor : Laurent Jonchère <>
Submitted on : Thursday, April 14, 2016 - 3:44:28 PM
Last modification on : Tuesday, October 6, 2020 - 3:10:06 AM
Long-term archiving on: : Tuesday, November 15, 2016 - 3:09:32 AM

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Kacem Chehdi, Akar Taher, Claude Cariou. Stable and unsupervised fuzzy C-means method and its validation in the context of multicomponent images. Journal of Electronic Imaging, SPIE and IS&T, 2015, 24 (6), pp.061117--061117. ⟨10.1117/1.JEI.24.6.061117⟩. ⟨hal-01300888⟩

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