Robust interval type-2 kernel-based possibilistic fuzzy deep local information clustering driven by Lambert-W function
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 no...
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| Published in | The Visual computer Vol. 40; no. 3; pp. 2161 - 2201 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.03.2024
Springer Nature B.V |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Peng, Siyun Wu, Chengmao Zhang, Xialu |
| Author_xml | – sequence: 1 givenname: Chengmao orcidid: 0000-0002-5881-4723 surname: Wu fullname: Wu, Chengmao organization: School of Electronic Engineering, Xi’an University of Posts and Telecommunications – sequence: 2 givenname: Siyun orcidid: 0000-0002-6562-8662 surname: Peng fullname: Peng, Siyun email: pengsiyun97@163.com organization: School of Electronic Engineering, Xi’an University of Posts and Telecommunications – sequence: 3 givenname: Xialu orcidid: 0000-0001-8563-8983 surname: Zhang fullname: Zhang, Xialu organization: School of Electronic Engineering, Xi’an University of Posts and Telecommunications |
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| Keywords | Image segmentation Possibilistic clustering Fuzzy clustering Local information Type-2 fuzzy set Non-local information Kernel metric Deep neighborhood window |
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| SubjectTerms | 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) |
| Title | Robust interval type-2 kernel-based possibilistic fuzzy deep local information clustering driven by Lambert-W function |
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