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K-Means Cluster Analysis for Image Segmentation

International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 96 - Number 4
Year of Publication: 2014
S. M. Aqil Burney
Humera Tariq

Aqil S M Burney and Humera Tariq. Article: K-Means Cluster Analysis for Image Segmentation. International Journal of Computer Applications 96(4):1-8, June 2014. Full text available. BibTeX

	author = {S. M. Aqil Burney and Humera Tariq},
	title = {Article: K-Means Cluster Analysis for Image Segmentation},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {96},
	number = {4},
	pages = {1-8},
	month = {June},
	note = {Full text available}


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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