Generalised kernel weighted fuzzy C-means clustering algorithm with local information

To improve the performance of segmentation for the images corrupted by noise, many variants of standard fuzzy C-means (FCM) clustering algorithm have been proposed that incorporate the local spatial neighbourhood information to perform image segmentation. Among them, the kernel weighted fuzzy local...

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Bibliographic Details
Published in:	Fuzzy sets and systems Vol. 340; pp. 91 - 108
Main Authors:	Memon, Kashif Hussain, Lee, Dong-Ho
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.06.2018
Subjects:	Enhanced clustering performance Kernel fuzzy c-means Local similarity Neighbourhood for higher dimensional input data Robustness to noise and outliers Local similarity Neighbourhood for higher dimensional input data Robustness to noise and outliers Kernel fuzzy c-means Enhanced clustering performance
ISSN:	0165-0114, 1872-6801
Online Access:	Get full text
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Summary:	To improve the performance of segmentation for the images corrupted by noise, many variants of standard fuzzy C-means (FCM) clustering algorithm have been proposed that incorporate the local spatial neighbourhood information to perform image segmentation. Among them, the kernel weighted fuzzy local information C-means (KWFLICM) algorithm gives robust to noise image segmentation results by using local spatial image neighbourhood information, it is limited to one-dimensional input data i.e. image intensity. In this paper, we propose a generalisation of KWFLICM (GKWFLICM) that is applicable to M-dimensional input data sets. The proposed algorithm incorporates neighbourhood information among the M-dimensional data, which mitigates the disadvantages of the standard FCM clustering algorithm (sensitive to noise and outliers, poor performance for differently sized clusters and for different density clusters) and greatly improves the clustering performance. Experiments have been performed on several noisy and non-noisy data sets, as well as natural and real-world images, to demonstrate the effectiveness, efficiency, and robustness to noise of the GKWFLICM algorithm by comparing it to kernel fuzzy C-means (KFCM), kernel possibilistic fuzzy C-means (KPFCM), fuzzy local information C-means (FLICM), and KWFLICM.
ISSN:	0165-0114 1872-6801
DOI:	10.1016/j.fss.2018.01.019