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K-Means Cluster Analysis for Image Segmentation

by S. M. Aqil Burney, Humera Tariq
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 96 - Number 4
Year of Publication: 2014
Authors: S. M. Aqil Burney, Humera Tariq
10.5120/16779-6360

S. M. Aqil Burney, Humera Tariq . K-Means Cluster Analysis for Image Segmentation. International Journal of Computer Applications. 96, 4 ( June 2014), 1-8. DOI=10.5120/16779-6360

@article{ 10.5120/16779-6360,
author = { S. M. Aqil Burney, Humera Tariq },
title = { K-Means Cluster Analysis for Image Segmentation },
journal = { International Journal of Computer Applications },
issue_date = { June 2014 },
volume = { 96 },
number = { 4 },
month = { June },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume96/number4/16779-6360/ },
doi = { 10.5120/16779-6360 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:20:50.535612+05:30
%A S. M. Aqil Burney
%A Humera Tariq
%T K-Means Cluster Analysis for Image Segmentation
%J International Journal of Computer Applications
%@ 0975-8887
%V 96
%N 4
%P 1-8
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Does K-Means reasonably divides the data into k groups is an important question that arises when one works on Image Segmentation? Which color space one should choose and how to ascertain that the k we determine is valid? The purpose of this study was to explore the answers to aforementioned questions. We perform K-Means on a number of 2-cluster, 3-cluster and k-cluster color images (k>3) in RGB and L*a*b* feature space. Ground truth (GT) images have been used to accomplish validation task. Silhouette analysis supports the peaks for given k-cluster image. Model accuracy in RGB space falls between 30% and 55% while in L*a*b* color space it ranges from 30% to 65%. Though few images used, but experimentation proves that K-Means significantly segment images much better in L*a*b* color space as compared to RGB feature space.

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Index Terms

Computer Science
Information Sciences

Keywords

Cluster evaluation L*a*b* Color Space Precision Recall Graph Image Segmentation

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