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To improve the robustness of fuzzy clustering algorithm with quadratic polynomial as the clustering center, a robust polynomial kernel-based fuzzy dual-local information clustering algorithm with quadratic polynomial as the clustering center is proposed in this paper. This paper will use quadratic polynomial to estimate the clustering center, which narrow the gap between the same category. It is different from traditional FCM algorithm, which takes a single point value as the clustering center. At the same time, the weighted fuzzy local information factor is introduced into clustering algorithm, and fuzzy membership is further filtered in the iterative process to improve the anti-noise performance of the algorithm. By optimizing the objective function of quadratic polynomial fuzzy local information C-means clustering, fuzzy membership and quadratic polynomial coefficients of the clustering center are derived theoretically, and the convergence of the algorithm is strictly proved by using Zangwill theorem and bordered Hessian matrix. Experimental results show that the proposed algorithm can give more attention to detail preservation while suppressing the noise compared with existing fuzzy clustering-related algorithms. [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]

Fuzzy clustering algorithm (FCM) can be directly used to segment images, it takes no account of the neighborhood information of the current pixel and does not have a robust segmentation noise suppression. Fuzzy Local Information C-means Clustering (FLICM) is a widely used robust segmentation algorithm, which combines spatial information with the membership degree of adjacent pixels. In order to further improve the robustness of FLICM algorithm, non-local information is embedded into FLICM algorithm and a fuzzy C-means clustering algorithm has local and non-local information (FLICMLNLI) is obtained. When calculating distance from pixel to cluster center, FLICMLNLI algorithm considers two distances from current pixel and its neighborhood pixels to cluster center. However, the algorithm gives the same weight to two different distances, which incorrectly magnifies the importance of neighborhood information in calculating the distance, resulting in unsatisfactory image segmentation effects and loss of image details. In order to solve this problem, we raise an improved self-learning weighted fuzzy algorithm, which directly obtains different weights in distance calculation through continuous iterative self-learning, then the distance metric with the weights obtained from self-learning is embedded in the objective function of the fuzzy clustering algorithm in order to improve the segmentation performance and robustness of the algorithm. A large number of experiments on different types of images show that the algorithm can not only suppress the noise but also retain the details in the image, the effect of segmenting complex noise images is better, and it provides better image segmentation results than the existing latest fuzzy clustering algorithms. [ABSTRACT FROM AUTHOR]