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

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IJCA Proceedings on International Conference on Advances in Emerging Technology
© 2016 by IJCA Journal
ICAET 2016 - Number 1
Year of Publication: 2016
Authors:
Sachin Singla
Baljeet Singh Khera

Sachin Singla and Baljeet Singh Khera. Article: A Comparative Study of K-Means, Fuzzy C-Means and Possibilistic Fuzzy C-Means Algorithm on Noisy Grayscale Images. IJCA Proceedings on International Conference on Advances in Emerging Technology ICAET 2016(1):1-5, September 2016. Full text available. BibTeX

@article{key:article,
	author = {Sachin Singla and Baljeet Singh Khera},
	title = {Article: A Comparative Study of K-Means, Fuzzy C-Means and Possibilistic Fuzzy C-Means Algorithm on Noisy Grayscale Images},
	journal = {IJCA Proceedings on International Conference on Advances in Emerging Technology},
	year = {2016},
	volume = {ICAET 2016},
	number = {1},
	pages = {1-5},
	month = {September},
	note = {Full text available}
}

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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