TY - JOUR
T1 - Robust Gaussian-base radial kernel fuzzy clustering algorithm for image segmentation
AU - Mújica-Vargas, Dante
AU - Carvajal-Gámez, Blanca
AU - Ochoa, Genaro
AU - Rubio, José
N1 - Publisher Copyright:
© The Institution of Engineering and Technology 2019
PY - 2019/7/25
Y1 - 2019/7/25
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85069945652&partnerID=8YFLogxK
U2 - 10.1049/el.2019.1281
DO - 10.1049/el.2019.1281
M3 - Artículo
AN - SCOPUS:85069945652
SN - 0013-5194
VL - 55
SP - 835
EP - 837
JO - Electronics Letters
JF - Electronics Letters
IS - 15
ER -