International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 4, No. 7, 8 Jun. 2012
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Review and Comparison of Kernel Based Fuzzy Image Segmentation Techniques
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Author(s)
Prabhjot Kaur
1,*
Pallavi Gupta
1
Poonam Sharma
1
Index Terms
Fuzzy Clustering, Fuzzy C-Means(FCM) algorithm, Kernel Fuzzy C-Means(KFCM), Intuitionistic Kernelized Fuzzy C-Means(KIFCM), Kernelized Type-II Fuzzy C-Means(KT2FCM), kernel width
Abstract
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 C-Means(FCM) algorithm, Kernel Fuzzy C-Means(KFCM), Intuitionistic Kernelized Fuzzy C-Means(KIFCM), Kernelized Type-II Fuzzy C-Means(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.
Cite This Paper
Prabhjot Kaur, Pallavi Gupta, Poonam Sharma, "Review and Comparison of Kernel Based Fuzzy Image Segmentation Techniques", International Journal of Intelligent Systems and Applications(IJISA), vol.4, no.7, pp.50-60, 2012. DOI:10.5815/ijisa.2012.07.07
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