Bearing performance degradation assessment based on information-theoretic metric learning and fuzzy c-means clustering

2020 ◽  
Vol 31 (7) ◽  
pp. 075001 ◽  
Author(s):  
Tong Qingjun ◽  
Hu Jianzhong
Keyword(s):  
Metric Learning ◽  
Performance Degradation ◽  
Information Theoretic ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

Bayesian distance metric learning for discriminative fuzzy c-means clustering

2018 ◽  
Vol 319 ◽  
pp. 21-33 ◽  
Author(s):  
Negar Heidari ◽  
Zahra Moslehi ◽  
Abdolreza Mirzaei ◽  
Mehran Safayani
Keyword(s):  
Metric Learning ◽  
Distance Metric Learning ◽  
Distance Metric ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

Multiresolution Bio-Medical Image Segmentation using Fuzzy C-Means Clustering

2019 ◽  
Vol 8 (4) ◽  
pp. 9548-9551
Keyword(s):  
Image Segmentation ◽  
Medical Image Segmentation ◽  
Experimental Result ◽  
Segmentation Method ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Robust Image ◽  
Multiple Clusters ◽  
Single Pixel ◽  
Popular Image

Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.

scholarly journals A Novel Coarse-to-fine Sea-land Segmentation Technique Based on Superpixel Fuzzy C-Means Clustering and Modified Chan-Vese Model

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Eman Elkhateeb ◽  
Hassan Soliman ◽  
Ahmed Atwan ◽  
Mohammed Elmogy ◽  
Kyung-Sup Kwak ◽  
...  
Keyword(s):  
Fuzzy C Means ◽  
Segmentation Technique ◽  
Fuzzy C Means Clustering ◽  
Coarse To Fine
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