2 research outputs found
Comparison of Various Improved-Partition Fuzzy c-Means Clustering Algorithms in Fast Color Reduction
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