Open AccessRobust Gaussian‐base radial kernel fuzzy clustering algorithm for image segmentation
Author(s) -
MújicaVargas Dante,
CarvajalGámez Blanca,
Ochoa Genaro,
Rubio José
Publication year - 2019
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2019.1281
Subject(s) - kernel (algebra) , cluster analysis , robustness (evolution) , pattern recognition (psychology) , mathematics , artificial intelligence , gaussian function , fuzzy logic , fuzzy clustering , image segmentation , pixel , algorithm , variable kernel density estimation , gaussian , segmentation , kernel method , computer science , support vector machine , biochemistry , chemistry , physics , combinatorics , quantum mechanics , gene
To perform the image segmentation task, in this Letter, a kernel fuzzy C‐means algorithm is introduced, strengthened by a robust Gaussian radial basis function kernel based on M‐estimators. It is well‐known that these kernels consider the squared difference as a similarity measure, which is not robust to atypical data. In this regard, the main motivation of this contribution is to improve the atypical information tolerance of these kernels, in order to make a better clustering of pixels. Experimental tests were developed considering colour images. The robustness and effectiveness of this proposal are verified by quantitative and qualitative results.