A stable and unsupervised Fuzzy C-Means for data classification

Akar Taher; Kacem Chehdi; Claude Cariou

doi:10.1117/12.2182595

30 April 2015 A stable and unsupervised Fuzzy C-Means for data classification

Akar Taher, Kacem Chehdi, Claude Cariou

Proceedings Volume 9534, Twelfth International Conference on Quality Control by Artificial Vision 2015; 953414 (2015) https://doi.org/10.1117/12.2182595
Event: The International Conference on Quality Control by Artificial Vision 2015, 2015, Le Creusot, France

Abstract

In this paper a stable and unsupervised version of FCM algorithm named FCMO is presented. The originality of the proposed FCMO algorithm relies: i) on the usage of an adaptive incremental technique to initialize the class centres that calls into question the intermediate initializations; this technique renders the algorithm stable and deterministic, and the classification results do not vary from a run to another, and ii) on the unsupervised evaluation criteria of the intermediate classification result to estimate the optimal number of classes; this makes the algorithm unsupervised. The efficiency of this optimized version of FCM is shown through some experimental results for its stability and its correct class number estimation.

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Akar Taher, Kacem Chehdi, and Claude Cariou "A stable and unsupervised Fuzzy C-Means for data classification", Proc. SPIE 9534, Twelfth International Conference on Quality Control by Artificial Vision 2015, 953414 (30 April 2015); https://doi.org/10.1117/12.2182595

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KEYWORDS

Expectation maximization algorithms

Fuzzy logic

Image classification

Detection and tracking algorithms

Vegetation

Hyperspectral imaging

Image sensors

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