Deep neighborhood structure driven interval type-2 kernel fuzzy c-means clustering with local versus non-local information

For images with high noise, existing robust fuzzy clustering-related methods are difficult to obtain satisfactory segmentation results. Hence, this paper proposes a novel single fuzzifier interval type-2 kernel-based fuzzy local and non-local information c-means clustering driven by a deep neighborh...

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Vydáno v:Multimedia tools and applications Ročník 82; číslo 28; s. 43455 - 43515
Hlavní autoři: Wu, Chengmao, Peng, Siyun
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.11.2023
Springer Nature B.V
Témata:
Algorithms
Clustering
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Image segmentation
Kernels
Multimedia Information Systems
Neighborhoods
Pixels
Robustness (mathematics)
Special Purpose and Application-Based Systems
Non-local information
Kernel metric
Image segmentation
Interval type-2 fuzzy sets
Fuzzy c-means clustering
Local information
ISSN:1380-7501, 1573-7721
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Shrnutí:For images with high noise, existing robust fuzzy clustering-related methods are difficult to obtain satisfactory segmentation results. Hence, this paper proposes a novel single fuzzifier interval type-2 kernel-based fuzzy local and non-local information c-means clustering driven by a deep neighborhood structure for strong noise image segmentation. Based on the neighborhood window around the current pixel, we firstly construct the novel deep neighborhood window structure, which is composed of neighborhood window around the current pixel and neighborhood window around pixels in the neighborhood window around the current pixel. Secondly maximally and minimally neighborhood weighted distances between current pixels and clustering centers are obtained through deep neighborhood window structure. Thirdly, two local neighborhood distances are used to modify upper and lower fuzzy membership of robust single fuzzifier interval type-2 fuzzy clustering with kernel metric and local versus non-local information, and an enhanced robust interval type-2 kernel-based fuzzy clustering with single fuzzifier is presented for strong noise image segmentation. Experimental results indicate that the proposed algorithm has better segmentation performance and stronger anti-noise robustness, and outperforms existing state-of-the-art robust fuzzy clustering-related algorithms in the presence of high noise. In particular, the segmentation accuracy of the proposed algorithm is 20% higher than that of the important KWFLICM algorithm and has increased by 10% compared with the FALRCM algorithm proposed in recent years.
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-023-15230-2