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Purpose: Multi-view fuzzy clustering algorithms are not widely used in image segmentation, and many of these algorithms are lacking in robustness. The purpose of this paper is to investigate a new algorithm that can segment the image better and retain as much detailed information about the image as possible when segmenting noisy images. Design/methodology/approach: The authors present a novel multi-view fuzzy c-means (FCM) clustering algorithm that includes an automatic view-weight learning mechanism. Firstly, this algorithm introduces a view-weight factor that can automatically adjust the weight of different views, thereby allowing each view to obtain the best possible weight. Secondly, the algorithm incorporates a weighted fuzzy factor, which serves to obtain local spatial information and local grayscale information to preserve image details as much as possible. Finally, in order to weaken the effects of noise and outliers in image segmentation, this algorithm employs the kernel distance measure instead of the Euclidean distance. Findings: The authors added different kinds of noise to images and conducted a large number of experimental tests. The results show that the proposed algorithm performs better and is more accurate than previous multi-view fuzzy clustering algorithms in solving the problem of noisy image segmentation. Originality/value: Most of the existing multi-view clustering algorithms are for multi-view datasets, and the multi-view fuzzy clustering algorithms are unable to eliminate noise points and outliers when dealing with noisy images. The algorithm proposed in this paper has stronger noise immunity and can better preserve the details of the original image. [ABSTRACT FROM AUTHOR]

Aiming at robust picture fuzzy clustering with weak anti-noise ability, which is difficult to meet the needs of high noise image segmentation. Hence, this paper proposes a robust dynamic semi-supervised picture fuzzy local information clustering with kernel metric and spatial information. Firstly, a robust weighted squared Euclidean distance between the current pixel and clustering center is constructed, and it is introduced into picture fuzzy clustering to form a robust spatial picture fuzzy clustering. Secondly, the membership degree of neighborhood pixels is linearly weighted to form local membership of the current pixel, which is used to supervise the picture fuzzy partition information of spatial picture fuzzy clustering, and a novel robust dynamic semi-supervised picture fuzzy clustering with spatial information is obtained. Subsequently, the fuzzy local information factor is embedded in dynamic semi-supervised spatial picture fuzzy clustering to obtain enhanced dynamic semi-supervised picture fuzzy local information clustering. Finally, to further improve the adaptability of dynamic semi-supervised spatial picture fuzzy local information clustering, a robust dynamic semi-supervised picture fuzzy local information clustering with kernel metric and spatial information constraints is proposed. Experimental results show that the proposed algorithm outperforms existing state-of-the-art robust picture fuzzy clustering-related algorithms for high noise image segmentation, and the Acc and PSNR indexes of the proposed algorithm in noisy images are significantly better than those of robust fuzzy clustering-related compared algorithms. [ABSTRACT FROM AUTHOR]

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 accuracy of intuitionistic fuzzy clustering-related algorithms, an intuitionistic fuzzy information-driven total Bregman divergence fuzzy clustering with multiple local information constraints is proposed in this paper. In this algorithm, a modified total Bregman divergence with spatial information constraints is firstly constructed to replace existing squared Euclidean distance in intuitionistic fuzzy clustering, which can further enhance the ability of noise suppression. By introducing an intuitionistic fuzzy weighted local information into the objective function of intuitionistic fuzzy c-means clustering, its noise sensitivity can be reduced and image details can be preserved effectively. Combining neighborhood spatial information, neighborhood gray information with the normalized variance of neighborhood pixels, a local similarity measure between the current pixel and its neighborhood pixels is constructed to better describe the influence degree of neighborhood pixels on the current pixel, and it is adaptively embedded into modified total Bregman divergence-based intuitionistic fuzzy clustering. In the end, a robust total Bregman divergence-based fuzzy weighted local information clustering algorithm motivated by intuitionistic fuzzy information of the image is obtained to solve the problem of robust image segmentation, and its convergence is strictly proved by the Zangwill theorem. Many experimental results show that the segmentation accuracy ACC of the proposed algorithm can be as high as more than 90%, and the misclassification rate ME can be as low as less than 28%. Therefore, the proposed algorithm has better segmentation performance and robustness than the existing state-of-the-art intuitionistic fuzzy clustering-related segmentation algorithm in the presence of noise. [ABSTRACT FROM AUTHOR]

Clustering algorithms with deep neural network has attracted wide attention to scholars. A deep fuzzy K-means clustering algorithm model on adaptive loss function and entropy regularization (DFKM) is proposed by combining automatic encoder and clustering algorithm. Although it introduces adaptive loss function and entropy regularization to improve the robustness of the model, its segmentation effect is not ideal for high noise. The research purpose of this paper is to focus on the anti-noise performance of image segmentation. Therefore, on the basis of DFKM, this paper focus on image segmentation, combine neighborhood median and mean information of current pixel, introduce neighborhood information of membership degree, and extend Euclidean distance to kernel space by using kernel function, propose a dual-neighborhood information constrained deep fuzzy clustering based on kernel function (KDFKMS). A large number of experimental results show that compared with DFKM and classical image segmentation algorithms, this algorithm has stronger anti-noise robustness. [ABSTRACT FROM AUTHOR]

Hyper‐spectral image (HSI) clustering has become a hot spot in remote sensing image research. However, due to the large amount and high dimension of HSI data, some traditional image clustering methods are no longer suitable, and even lead to poor clustering performance and long computing time. Therefore, this paper proposes a novel clustering method for HSI to make up for the shortcomings of traditional clustering methods. Firstly, a fuzzy similarity matrix is constructed by using the bipartite graph to obtain low‐dimensional hyper‐spectral data, which can reduce the complexity and the operation time of the algorithm. Secondly, fuzzy membership mapping is carried out through the constructed bipartite graph, and a non‐negative regularization term is added to limit ill‐conditioned problems. Thirdly, this paper uses the Geman‐McClure function to optimize Euclidean distance. Finally, in this paper, the spectral information of HSI is considered while the neighbourhood information of HSI is added to achieve the unity of space spectrum, which enhances the robustness of the algorithm and improves the clustering performance of the algorithm. Compared with existing HSI clustering algorithms, the proposed algorithm has higher clustering accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Aiming at the problem that existing robust fuzzy clustering algorithms are still sensitive to high noise, a total Bregman divergence-driven possibilistic fuzzy clustering with multiple information constraints and kernel metric (TSKFLICM) is proposed in this article. First, we introduce total Bregman divergence (TBD) to overcome the shortcoming that Bregman divergence is variant with rotation. Second, polynomial kernel function is used to kernelize TBD, and kernelized TBD is embedded with neighborhood information of pixels to further enhance its ability to suppress noise. Finally, kernelized TBD with spatial information constraints is combined with possibilistic typicality to construct the novel objective function of possibilistic fuzzy clustering, and a TBD-driven kernel possibilistic fuzzy clustering with multiple information constraints is obtained through optimization theory. The effectiveness of the proposed algorithm for noisy image segmentation is explained by means of sample weighted clustering method. Experimental results show that compared with other algorithms, the Jaccard score, segmentation accuracy, and peak signal-to-noise ratio of the proposed algorithm are improved by 0.017–0.481, 1.327%–41.260%, and 2.416–11.765, respectively. Therefore, the TSKFLICM algorithm has better segmentation performance and stronger antinoise ability than the existing state-of-the-art fuzzy clustering algorithm. [ABSTRACT FROM AUTHOR]

Gaussian kernel is defined by Euclidean distance and has been widely used in many fields. In the view of Euclidean distance is sensitive to outliers or noise and it is difficult to obtain satisfactory results for complex non-convex data. Entropy-like divergence is firstly induced by combining Jenson-Shannon/Bregman divergence with convex function, and its mercer kernel function called entropy-like divergence-based kernel is also constructed in this paper. Secondly, a new fuzzy weighted factor based on entropy-like divergence-based kernel is proposed by improving the existing trade-off weighting factor of kernel-based fuzzy local information C-means clustering (KWFLICM). In the end, a weighted fuzzy local information clustering based on entropy-like divergence-based kernel (EKWFLICM) is presented, which combines a new weighted fuzzy factor and entropy-like divergence-based kernel. Experiment results show that the proposed algorithm outperforms the segmentation performance of existing state-of-the-art fuzzy clustering-related algorithms for the image in presence of high noise. • An entropy-like divergence and its mercer kernel function are presented. • A novel divergence-based kernel fuzzy clustering is proved to be convergent. • A kernel weighted fuzzy clustering based on entropy-like divergence is constructed. • Proposed algorithm outperforms existing KWFLICM and ILKFCM for image segmentation. [ABSTRACT FROM AUTHOR]