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
Online-Zugang:	Volltext
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