Generalised kernel weighted fuzzy C-means clustering algorithm with local information

Memon, Kashif Hussain; Lee, Dong-Ho

doi:10.1016/j.fss.2018.01.019

Cited by 48 publications

(14 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, the adaptive fuzzy clustering method (AFCM) and the Mahalanobis distance are combined, and the Mahalanobis distance is used to represent the measurement of the distance of the roller vibration data of the processing equipment. The fuzzy partition matrix and the category center of characteristic parameter x t can be obtained by solving the minimum value of the objective function (formula (1)) of the c fuzzy partitions of the various parameters of the roller vibration data [7,8].…”

Section: Derivation Of Calculation Model For Performance Degradation mentioning

confidence: 99%

Research on Prediction Method of Performance Degradation of Flexible Optoelectronic Film Material Processing Equipment Based on Adaptive Fuzzy Clustering

Deng

Yao

Lu³

2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Flexible photoelectric film is an anisotropic material. The slight change of equipment performance during processing is prone to cause deformation of the material. Therefore, it is important to predict the degradation of processing equipment performance. Since the performance degradation of flexible photoelectric film material Roll-to-Roll (R2R) processing equipment is a nonlinear process, this paper introduces an adaptive fuzzy clustering method to construct a fuzzy membership function model for calculating the performance degradation index of R2R processing equipment and studies the parameter solving method such as the AFCM division of the roller vibration data, the category center value of the fuzzy membership function, and the input data division area width. Finally, the performance degradation index calculation algorithm is designed. The roller shaft accelerated life test was carried out using self-made equipment. The test data were 1000 sets. The results showed that the root mean square eigenvalues and the kurtosis eigenvalues of the roller vibration data are sensitive to the performance degradation. The equipment performance curve described by the first and second types of performance degradation indicators was very stable in the early stage. After the 800th group, the curve continued to decrease, and the change was more severe, indicating that the performance degradation of the equipment is more serious. In the 980th group, the longer-lasting roller shaft was damaged, and the performance index value was about zero, which proved the correctness of the performance degradation prediction method proposed in this paper in calculating the performance degradation value of the equipment.

show abstract

Section: Derivation Of Calculation Model For Performance Degradation mentioning

confidence: 99%

Research on Prediction Method of Performance Degradation of Flexible Optoelectronic Film Material Processing Equipment Based on Adaptive Fuzzy Clustering

Deng

Yao

Lu³

2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…It is well known that making use of spatial information is a critical method to improve segmentation accuracy, and owing to the complexity in image segmentation tasks, some scholars proposed other methods from different points of views. For example, Zhang and Chen 9 introduced kernel function into FCM algorithm to investigate the influence in specific image segmentation tasks and proposed KFCM algorithm which was applied in nonlinear situation, Gong et al 10 merged kernel function with local information and proposed KWFLICM algorithm which adopted Gaussian Radical Basis kernel function to define non‐Euclidean distance, Memon and Lee 11 generalized KWFLICM algorithm and proposed generalized KWFLICM (GKWFLICM) algorithm which could handle m ‐dimensional data and was successfully applied in multiple tasks. It needs to point out that genetic algorithm (GA) and deep learning (DL) at present are also useful tools for image segmentation and have become more and more popular, some researchers have proposed some different variants of GA and breaking state of the art (SOTA) of image segmentation, respectively.…”

Section: Introductionmentioning

confidence: 99%

Pythagorean fuzzy C‐means algorithm for image segmentation

Zeng

Song

et al. 2020

Int J Intell Syst

View full text Add to dashboard Cite

In recent decades, image segmentation has aroused great interest of many researchers, and has become an important part of machine learning, pattern recognition, and computer vision. Among many methods of image segmentation, fuzzy C-means (FCM) algorithm is undoubtedly a milestone in unsupervised method. With the further study of FCM, various different kinds of FCM algorithms are put forward to deal with the specific problems in image segmentation. Because there exist uncertainties in different regions of the image and similarity in the same region, reducing the uncertainty is still the main problem in image segmentation. Considering that Pythagorean fuzzy set (PFS) is a powerful tool to deal with uncertainty, in this paper, we use PFS to describe the uncertainty of image segmentation, including introducing fuzzification and defuzzification process and Pythagorean fuzzy element to describe the membership degree of pixel, combine the neighborhood information with weights and Pythagorean fuzzy distance, and propose Pythagorean fuzzy C-means (PFCM) algorithm. Finally, we apply PFCM algorithm in image segmentation, such as different size images and Berkeley Segmentation Data Set to illustrate the effectiveness and applicability of our proposed algorithm. Meanwhile, we do comparison analysis between PFCM, fully convolution network

show abstract

“…Fuzzy Local Information C-Means (FLICM) [20] and Reformulated FLICM (RFLICM) [21] defined the local fuzzy factor described by the spatial distance and the local variation coefficient of central pixel respectively. Although the noise immunity is improved obviously, there are still a lot of mis-segmented pixels [22]. In the statistical framework, on the basis of using probability distribution to describe the random characteristics of the spectra to reduce sensitivity, Markov Random Fields (MRF) is also utilized to establish the prior probability based on the neighborhood system to further enhance the robustness [23].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Distance-Weighted Voronoi Tessellation for Remote Sensing Image Segmentation

Chen

Zhao

et al. 2020

Remote Sensing

View full text Add to dashboard Cite

The spatial fragmentation of high-resolution remote sensing images makes the segmentation algorithm put forward a strong demand for noise immunity. However, the stronger the noise immunity, the more serious the loss of detailed information, which easily leads to the neglect of effective characteristics. In view of the difficulty of balancing the noise immunity and effective characteristic retention, an adaptive distance-weighted Voronoi tessellation technology is proposed for remote sensing image segmentation. The distance between pixels and seed points in Voronoi tessellation is established by the adaptive weighting of spatial distance and spectral distance. The weight coefficient used to control the influence intensity of spatial distance is defined by a monotone decreasing function. Following the fuzzy clustering framework, a fuzzy segmentation model with Kullback–Leibler (KL) entropy regularization is established by using multivariate Gaussian distribution to describe the spectral characteristics and Markov Random Field (MRF) to consider the neighborhood effect of sub-regions. Finally, a series of parameter optimization schemes are designed according to parameter characteristics to obtain the optimal segmentation results. The proposed algorithm is validated on many multispectral remote sensing images with five comparing algorithms by qualitative and quantitative analysis. A large number of experiments show that the proposed algorithm can overcome the complex noise as well as better ensure effective characteristics.

show abstract

Generalised kernel weighted fuzzy C-means clustering algorithm with local information

Cited by 48 publications

References 33 publications

Research on Prediction Method of Performance Degradation of Flexible Optoelectronic Film Material Processing Equipment Based on Adaptive Fuzzy Clustering

Research on Prediction Method of Performance Degradation of Flexible Optoelectronic Film Material Processing Equipment Based on Adaptive Fuzzy Clustering

Pythagorean fuzzy C‐means algorithm for image segmentation

Adaptive Distance-Weighted Voronoi Tessellation for Remote Sensing Image Segmentation

Contact Info

Product

Resources

About