Comparison of Various Improved-Partition Fuzzy c-Means Clustering Algorithms in Fast Color Reduction

Szilágyi, L. and Dénesi, G. and Kovács, Levente and Szilágyi, S. M. (2014) Comparison of Various Improved-Partition Fuzzy c-Means Clustering Algorithms in Fast Color Reduction. In: SISY 2014. IEEE Hungary Section, Subotica, pp. 197-202. ISBN 978-1-4799-5995-2

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Abstract

This paper provides a comparative study of sev- eral enhanced versions of the fuzzy c -means clustering al- gorithm in an application of histogram-based image color reduction. A common preprocessing is performed before clus- tering, consisting of a preliminary color quantization, histogram extraction and selection of frequently occurring colors of the image. These selected colors will be clustered by tested c -means algorithms. Clustering is followed by another common step, which creates the output image. Besides conventional hard (HCM) and fuzzy c -means (FCM) clustering, the so-called generalized improved partition FCM algorithm, and several versions of the suppressed FCM (s-FCM) in its conventional and generalized form, are included in this study. Accuracy is measured as the average color difference between pixels of the input and output image, while efficiency is mostly characterized by the total runtime of the performed color reduction. Nu- merical evaluation found all enhanced FCM algorithms more accurate, and four out of seven enhanced algorithms faster than FCM. All tested algorithms can create reduced color images of acceptable quality.

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