Intuitionistic fuzzy information-driven total Bregman divergence fuzzy clustering with multiple local information constraints for image segmentation

Aiming at the shortcoming of existing robust intuitionistic fuzzy clustering and its variant algorithms in the presence of high noise, we will explore a novel intuitionistic fuzzy clustering-related segmentation algorithm with strong robustness. To enhance the anti-noise robustness and segmentation...

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Vydáno v:	The Visual computer Ročník 39; číslo 1; s. 149 - 181
Hlavní autoři:	Wu, Chengmao, Huang, Congcong, Zhang, Jiajia
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2023 Springer Nature B.V
Témata:	Algorithms Artificial Intelligence Clustering Computer Graphics Computer Science Divergence Euclidean geometry Fuzzy sets Genetic algorithms Image Processing and Computer Vision Image segmentation Neighborhoods Noise reduction Noise sensitivity Original Article Pixels Robustness (mathematics) Set theory Spatial data Statistical analysis Vision systems Image segmentation Local similarity measure Intuitionistic fuzzy clustering Intuitionistic fuzzy weighted local information factor Total Bregman divergence
ISSN:	0178-2789, 1432-2315
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