Open AccessAn Improved PSO-FCM Algorithm for Image Segmentation
Author(s) -
Peng Xia,
Lin Yao,
Lihua Zhang
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/267/4/042081
Subject(s) - image segmentation , particle swarm optimization , cluster analysis , segmentation , artificial intelligence , computer science , image (mathematics) , algorithm , segmentation based object categorization , pattern recognition (psychology) , value (mathematics) , local optimum , noise (video) , scale space segmentation , machine learning
FCM algorithm is a kind of soft clustering method widely used in image segmentation, but the algorithm is prone to fall into local minimum and sensitive to initial value. In this paper, considering the local and global optimization capabilities, an improved PSO algorithm is proposed. Image segmentation experimental results show that the improved algorithm not only can effectively avoid the local optimal value due to the manual setting of FCM initial value, but also has better accuracy and anti-noise performance than traditional FCM.