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Soft Computing

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09.12.2019 | Methodologies and Application

Robust credibilistic intuitionistic fuzzy clustering for image segmentation

verfasst von: Chengmao Wu, Xiaoqiang Yang

Erschienen in: Soft Computing | Ausgabe 14/2020

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Abstract

To improve the anti-noise ability of credibilistic intuitionistic fuzzy c-means clustering method (CIFCM) for image segmentation, this paper proposes a robust credibilistic intuitionistic fuzzy c-means clustering method based on credibility of pixels and intuitionistic fuzzy entropy. Firstly, a new similarity measure is constructed by utilizing the grayscale and spatial relationship between the current pixel and its neighborhood pixels. Secondly, it is embedded into the objective function of credibilistic intuitionistic fuzzy c-means clustering, and a new robust clustering method with spatial constraints is presented to effectively solve the segmentation problem of image corrupted by high noise. In the end, the convergence of this proposed robust clustering method is strictly proved by iterated convergence theorem. Experimental results show that proposed algorithm has better noise-suppression ability and more satisfactory segmentation results than CIFCM algorithm.

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Titel: Robust credibilistic intuitionistic fuzzy clustering for image segmentation
verfasst von: Chengmao Wu
Xiaoqiang Yang
Publikationsdatum: 09.12.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 14/2020
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-019-04593-0

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