NDLI: Generalised fuzzy c-means clustering algorithm with local information

Content Provider	IET Digital Library
Author	Memon, Kashif Hussain Lee, Dong Ho
Abstract	Much research has been conducted on fuzzy c-means (FCM) clustering algorithms for image segmentation that incorporate the local neighbourhood information into their objective function in order to mitigate problems related to noise sensitivity and poor performance. Although the bias-corrected FCM, FCM with spatial constraints, and adaptive weighted averaging algorithms have proven to be robust to noise for image segmentation using local spatial image information, they have some disadvantages: (i) they are limited to single feature input data (i.e. intensity level feature), (ii) their robustness to noise and effectiveness heavily depend on a crucial parameter α, and (iii) it is difficult to find the optimal value of α, which is generally selected experimentally. In this study, to overcome all of these disadvantages, the authors present a generalisation of these types of algorithms that is applicable to cluster M-features input data. The proposed generalised FCM clustering algorithm with local information (GFCMLI) not only mitigates the disadvantages of standard FCM, but also highly improves the overall clustering performance. Experiments have been performed on several noisy data and natural/real-world images in order to demonstrate the effectiveness, efficiency, and robustness to noise of the GFCMLI algorithm as compared with conventional methods.
Starting Page	1
Ending Page	12
Page Count	12
ISSN	17519659
Volume Number	11
e-ISSN	17519667
Issue Number	Issue 1, Jan (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/11/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2016.0282
Journal	IET Image Processing
Publisher Date	2016-08-18
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Weighted Averaging Algorithm Bias-corrected FCM Cluster M-features Combinatorial Mathematics Computer Vision And Image Processing Technique Feature Extraction Fuzzy Set Theory Generalised FCM Clustering Algorithm Generalised Fuzzy C-means Clustering Algorithm GFCMLI Algorithm Image Recognition Image Segmentation Intensity Level Feature Local Neighbourhood Information Local Spatial Image Information Natural-real-world Image Noise Sensitivity Optimal Value Pattern Clustering Spatial Constraint
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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