Generalised fuzzy c-means clustering algorithm with local information

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, FC...

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Veröffentlicht in:IET image processing Jg. 11; H. 1; S. 1 - 12
Hauptverfasser: Memon, Kashif Hussain, Lee, Dong-Ho
Format: Journal Article
Sprache:Englisch
Veröffentlicht: The Institution of Engineering and Technology 01.01.2017
Schlagworte:
adaptive weighted averaging algorithm
Algorithms
bias‐corrected FCM
cluster M‐features
Clustering
Efficiency
feature extraction
Fuzzy
fuzzy set theory
generalised FCM clustering algorithm
generalised fuzzy c‐means clustering algorithm
GFCMLI algorithm
Image segmentation
intensity level feature
local neighbourhood information
local spatial image information
natural‐real‐world images
Noise
noise sensitivity
optimal value
pattern clustering
Research Article
Robustness
spatial constraint
bias-corrected FCM
fuzzy set theory
local spatial image information
natural-real-world images
generalised fuzzy c-means clustering algorithm
spatial constraint
GFCMLI algorithm
local neighbourhood information
pattern clustering
image segmentation
feature extraction
optimal value
adaptive weighted averaging algorithm
intensity level feature
noise sensitivity
cluster M-features
generalised FCM clustering algorithm
ISSN:1751-9659, 1751-9667
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Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:1751-9659
1751-9667
DOI:10.1049/iet-ipr.2016.0282