Review and Comparison of Kernel Based Fuzzy Image Segmentation Techniques | Zendy

Prabhjot Kaur﻿ | Zendy; Pallavi Gupta | Zendy; Poonam Sharma | Zendy

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Review and Comparison of Kernel Based Fuzzy Image Segmentation Techniques

Author(s) -

Prabhjot Kaur,

Pallavi Gupta,

Poonam Sharma

Publication year - 2012

Publication title -

international journal of intelligent systems and applications

Language(s) - English

Resource type - Journals

eISSN - 2074-9058

pISSN - 2074-904X

DOI - 10.5815/ijisa.2012.07.07

Subject(s) - fuzzy clustering , pattern recognition (psychology) , fuzzy logic , artificial intelligence , kernel (algebra) , computer science , fuzzy classification , cluster analysis , mathematics , data mining , fuzzy set , discrete mathematics

This paper presents a detailed study and comparison of some Kernelized Fuzzy C-means Clustering based image segmentation algorithms Four algorithms have been used Fuzzy Clustering, Fuzzy CMeans(FCM) algorithm, Kernel Fuzzy CMeans(KFCM), Intuitionistic Kernelized Fuzzy CMeans(KIFCM), Kernelized Type-II Fuzzy CMeans(KT2FCM).The four algorithms are studied and analyzed both quantitatively and qualitatively. These algorithms are implemented on synthetic images in case of without noise along with Gaussian and salt and pepper noise for better review and comparison. Based on outputs best algorithm is suggested. Index Terms —Fuzzy Clustering, Fuzzy CMeans(FCM) algorithm, Kernel Fuzzy CMeans(KFCM),Intuitionistic Kernelized Fuzzy CMeans(KIFCM) ,Kernelized Type-II Fuzzy CMeans(KT2FCM),kernel width.

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