Coarse–fine surrogate model driven multiobjective evolutionary fuzzy clustering algorithm with dual memberships for noisy image segmentation

To improve the segmentation performance and boost evolutionary efficiency of multiobjective evolutionary clustering algorithms on noisy images, this paper proposes a coarse–fine surrogate model driven multiobjective evolutionary fuzzy clustering algorithm with dual membership functions (CFS-MOEFC)....

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Vydané v:	Applied soft computing Ročník 112; s. 107778
Hlavní autori:	Zhao, Feng, Liu, Feifan, Li, Chaoqi, Liu, Hanqiang, Lan, Rong, Fan, Jiulun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.11.2021
Predmet:	Coarse–fine surrogate model Dual fuzzy memberships Fuzzy clustering Image segmentation Multiobjective evolutionary optimization Image segmentation Coarse–fine surrogate model Fuzzy clustering Dual fuzzy memberships Multiobjective evolutionary optimization
ISSN:	1568-4946, 1872-9681
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