Robust interval type-2 kernel-based possibilistic fuzzy deep local information clustering driven by Lambert-W function

• Published in: The Visual computer Vol. 40; no. 3; pp. 2161 - 2201
• Main Authors: Wu, Chengmao, Peng, Siyun, Zhang, Xialu
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2024; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Clustering; Computer Graphics; Computer Science; Fuzzy sets; Image enhancement; Image Processing and Computer Vision; Neighborhoods; Noise sensitivity; Original Article; Remote sensing; Robustness (mathematics); Image segmentation; Possibilistic clustering; Fuzzy clustering; Local information; Type-2 fuzzy set; Non-local information; Kernel metric; Deep neighborhood window
• ISSN: 0178-2789; 1432-2315
• DOI: 10.1007/s00371-023-02910-1

Interval type-2 fuzzy sets not only have stronger ability to deal with uncertainty, but also have low computational complexity than general type-2 fuzzy set, so they are widely used in fuzzy clustering methods. However, most existing interval type-2 fuzzy clustering methods are still sensitive to noise and lack a certain degree of robustness in segmenting images with noise. Therefore, this paper proposes a novel interval type-2 enhanced kernel possibilistic fuzzy local and non-local information c-means clustering method for segmenting images with high noise. Interval type-2 possibilistic fuzzy clustering with Lambert-W function is first extended to obtain a novel interval type-2 enhanced possibilistic fuzzy clustering with product partition. Then deep local neighborhood information including local and non-local information is used to constrain interval type-2 enhanced possibilistic fuzzy product partition c-means clustering, and a robust interval type-2 enhanced possibilistic fuzzy deep local information clustering with kernel metric is proposed. Experimental results demonstrate that the proposed algorithm significantly outperforms the latest fuzzy clustering-related algorithms in the presence of high noise.