Kernel-Based Fuzzy Local Information Clustering Algorithm Self-integrating Non-Local Information

2021 ◽  
Author(s):  
Qiuyu Song ◽  
Chengmao Wu ◽  
Xiaoping Tian ◽  
Yue Song ◽  
Xiaokang Guo
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Integration Method ◽  
Clustering Algorithms ◽  
Noise Removal ◽  
Local Information ◽  
Gaussian Kernel ◽  
Distance Metric ◽  
High Noise ◽  
Non Local

Abstract The application of fuzzy clustering algorithms in image segmentation is a hot research topic nowadays. Existing fuzzy clustering algorithms have the following three problems: (1)The parameters of spatial information constraints can$'$t be selected adaptively; (2)The image corrupted by high noise can$'$t be segmented effectively; (3)It is difficult to achieve a balance between noise removal and detail preservation. In the fuzzy clustering based on the optimization model, the choice of distance metric is very important. Since the use of Euclidean distance will lead to sensitivity to outliers and noise, it is difficult to obtain satisfactory segmentation results, which will affect the clustering performance. This paper proposes an optimization algorithm based on the kernel-based fuzzy local information clustering integrating non-local information (KFLNLI). The algorithm adopts a self-integration method to introduce local and non-local information of images, which solves the common problems of current clustering algorithm. Firstly, the self-integration method solves the problem of selecting spatial constraint parameters. The algorithm uses continuous self-learning iteration to calculate the weight coefficients; Secondly, the distance metric uses Gaussian kernel function to induce the distance to further enhance the robustness against noise and the adaptivity of processing different images; Finally, both local and non-local information are introduced to achieve a segmentation effect that can eliminate most of the noise and retain the original details of the image. Experimental results show that the algorithm is superior to existing state-of-the-art fuzzy clustering-related algorithm in the presence of high noise.

Improved fuzzy clustering algorithm with non-local information for image segmentation

2016 ◽  
Vol 76 (6) ◽  
pp. 7869-7895 ◽  
Author(s):  
Xiaofeng Zhang ◽  
Yujuan Sun ◽  
Gang Wang ◽  
Qiang Guo ◽  
Caiming Zhang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Local Information ◽  
Fuzzy Clustering Algorithm ◽  
Non Local

scholarly journals A novel self-learning weighted fuzzy local information clustering algorithm integrating local and non-local spatial information for noise image segmentation

2021 ◽  
Author(s):  
Qiuyu Song ◽  
Chengmao Wu ◽  
Xiaoping Tian ◽  
Yue Song ◽  
Xiaokang Guo
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Spatial Information ◽  
Local Information ◽  
Cluster Center ◽  
Fuzzy Clustering Algorithm ◽  
Robust Segmentation ◽  
Non Local ◽  
Self Learning

AbstractFuzzy 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.

scholarly journals Improved fuzzy clustering for image segmentation based on a low-rank prior

2021 ◽  
Author(s):  
Xiaofeng Zhang ◽  
Hua Wang ◽  
Yan Zhang ◽  
Xin Gao ◽  
Gang Wang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Disease Diagnosis ◽  
Local Information ◽  
Low Rank ◽  
Improve Efficiency ◽  
Non Local

AbstractImage segmentation is a basic problem in medical image analysis and useful for disease diagnosis. However, the complexity of medical images makes image segmentation difficult. In recent decades, fuzzy clustering algorithms have been preferred due to their simplicity and efficiency. However, they are sensitive to noise. To solve this problem, many algorithms using non-local information have been proposed, which perform well but are inefficient. This paper proposes an improved fuzzy clustering algorithm utilizing nonlocal self-similarity and a low-rank prior for image segmentation. Firstly, cluster centers are initialized based on peak detection. Then, a pixel correlation model between corresponding pixels is constructed, and similar pixel sets are retrieved. To improve efficiency and robustness, the proposed algorithm uses a novel objective function combining non-local information and a low-rank prior. Experiments on synthetic images and medical images illustrate that the algorithm can improve efficiency greatly while achieving satisfactory results.

AN EXTENDED FUZZY CLUSTERING ALGORITHM AND ITS APPLICATION

1995 ◽  
Vol 05 (02) ◽  
pp. 239-259
Author(s):  
SU HWAN KIM ◽  
SEON WOOK KIM ◽  
TAE WON RHEE
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Main Memory ◽  
Color Image Segmentation ◽  
Occurrence Rate ◽  
Secondary Memory ◽  
Worst Case ◽  
Memory Space ◽  
Fuzzy Clustering Algorithm

For data analyses, it is very important to combine data with similar attribute values into a categorically homogeneous subset, called a cluster, and this technique is called clustering. Generally crisp clustering algorithms are weak in noise, because each datum should be assigned to exactly one cluster. In order to solve the problem, a fuzzy c-means, a fuzzy maximum likelihood estimation, and an optimal fuzzy clustering algorithms in the fuzzy set theory have been proposed. They, however, require a lot of processing time because of exhaustive iteration with an amount of data and their memberships. Especially large memory space results in the degradation of performance in real-time processing applications, because it takes too much time to swap between the main memory and the secondary memory. To overcome these limitations, an extended fuzzy clustering algorithm based on an unsupervised optimal fuzzy clustering algorithm is proposed in this paper. This algorithm assigns a weight factor to each distinct datum considering its occurrence rate. Also, the proposed extended fuzzy clustering algorithm considers the degree of importances of each attribute, which determines the characteristics of the data. The worst case is that the whole data has an uniformly normal distribution, which means the importance of all attributes are the same. The proposed extended fuzzy clustering algorithm has better performance than the unsupervised optimal fuzzy clustering algorithm in terms of memory space and execution time in most cases. For simulation the proposed algorithm is applied to color image segmentation. Also automatic target detection and multipeak detection are considered as applications. These schemes can be applied to any other fuzzy clustering algorithms.

scholarly journals An Adaptive Multiobjective Genetic Algorithm with Fuzzy c-Means for Automatic Data Clustering

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Ze Dong ◽  
Hao Jia ◽  
Miao Liu
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Majority Vote ◽  
Clustering Algorithms ◽  
Nsga Ii ◽  
Number Of Clusters ◽  
Automatic Data ◽  
Multiobjective Genetic Algorithm ◽  
Fuzzy Clustering Method

This paper presents a fuzzy clustering method based on multiobjective genetic algorithm. The ADNSGA2-FCM algorithm was developed to solve the clustering problem by combining the fuzzy clustering algorithm (FCM) with the multiobjective genetic algorithm (NSGA-II) and introducing an adaptive mechanism. The algorithm does not need to give the number of clusters in advance. After the number of initial clusters and the center coordinates are given randomly, the optimal solution set is found by the multiobjective evolutionary algorithm. After determining the optimal number of clusters by majority vote method, the Jm value is continuously optimized through the combination of Canonical Genetic Algorithm and FCM, and finally the best clustering result is obtained. By using standard UCI dataset verification and comparing with existing single-objective and multiobjective clustering algorithms, the effectiveness of this method is proved.

scholarly journals COMBINING FUZZY PROBABILITY AND FUZZY CLUSTERING FOR MULTISPECTRAL SATELLITE IMAGERY CLASSIFICATION

2016 ◽  
Vol 54 (3) ◽  
pp. 300 ◽  
Author(s):  
Mai Dinh Sinh ◽  
Le Hung Trinh ◽  
Ngo Thanh Long
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Satellite Image ◽  
Great Influence ◽  
Unsupervised Classification ◽  
Classification Algorithms ◽  
Fuzzy Probability ◽  
Multispectral Satellite Images ◽  
The Stability

This paper proposes a method of combining fuzzy probability and fuzzy clustering algorithm to classify on multispectral satellite images by relying on fuzzy probability to calculate the number of clusters and the centroid of clusters then using fuzzy clustering to classifying land-cover on the satellite image. In fact, the classification algorithms, the initialization of the clusters and the initial centroid of clusters have great influence on the stability of the algorithms, dealing time and classification results; the unsupervised classification algorithms such as k-Means, c-Means, Iso-data are used quite common for many problems, but the disadvantages is the low accuracy and unstable, especially when dealing with the problems on the satellite image. Results of the algorithm which are proposed show significant reduction of noise in the clusters and comparison with various clustering algorithms like k-means, iso-data, so on. 

scholarly journals Robust Deep Kernel-Based Fuzzy Clustering With Spatial Information for Image Segmentation

2021 ◽  
Author(s):  
Lujia Lei ◽  
Chengmao Wu ◽  
Xiaoping Tian
Keyword(s):  
Image Segmentation ◽  
Kernel Function ◽  
Loss Function ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Spatial Information ◽  
Clustering Algorithms ◽  
Noise Robustness ◽  
Neighborhood Information ◽  
Classical Image

Abstract Clustering algorithms with deep neural network have attracted wide attention of scholars. A deep fuzzy K-means clustering algorithm model with 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; At the same time, its model does not use a convolutional auto-encoder, which is not suitable for high-dimensional images.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.

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