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A Comparative Study of K-Means, Fuzzy C-Means and Possibilistic Fuzzy C-Means Algorithm on Noisy Grayscale Images

Published on September 2016 by Sachin Singla, Baljeet Singh Khera
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International Conference on Advances in Emerging Technology
Foundation of Computer Science USA
ICAET2016 - Number 1
September 2016
Authors: Sachin Singla, Baljeet Singh Khera
f4bf830b-23ff-4f90-ac33-febbeccdc9f7

Sachin Singla, Baljeet Singh Khera . A Comparative Study of K-Means, Fuzzy C-Means and Possibilistic Fuzzy C-Means Algorithm on Noisy Grayscale Images. International Conference on Advances in Emerging Technology. ICAET2016, 1 (September 2016), 1-5.

@article{
author = { Sachin Singla, Baljeet Singh Khera },
title = { A Comparative Study of K-Means, Fuzzy C-Means and Possibilistic Fuzzy C-Means Algorithm on Noisy Grayscale Images },
journal = { International Conference on Advances in Emerging Technology },
issue_date = { September 2016 },
volume = { ICAET2016 },
number = { 1 },
month = { September },
year = { 2016 },
issn = 0975-8887,
pages = { 1-5 },
numpages = 5,
url = { /proceedings/icaet2016/number1/25875-t017/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Advances in Emerging Technology
%A Sachin Singla
%A Baljeet Singh Khera
%T A Comparative Study of K-Means, Fuzzy C-Means and Possibilistic Fuzzy C-Means Algorithm on Noisy Grayscale Images
%J International Conference on Advances in Emerging Technology
%@ 0975-8887
%V ICAET2016
%N 1
%P 1-5
%D 2016
%I International Journal of Computer Applications
Abstract

Clustering is used to arrange the graphic data in the cluster unsupervised learning methods. Clustering is used in the field of image processing to identifying objects that have same features in an image. Clustering can be categorized into Hard and Fuzzy clustering scheme. This article discusses the study of hard clustering based Standard K-Means and different soft (fuzzy) clustering algorithm exits such as Fuzzy C-Means (FCM) and Possibilistic Fuzzy C-Means (PFCM). These algorithms are used to segment and analyse the standard and coloured images but this research work deals with noisy grayscale images. PSNR, MSE and SSIM are used as evaluation parameter to compare the K-Means, FCM and PFCM results. Finally, the experimental results proved that PFCM favorable over FCM and K-Means.

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Index Terms

Computer Science
Information Sciences

Keywords

Image Segmentation Clustering K-means Fcm Pfcm.