Paper Info
Title
A Novel Image Segmentation Method Based on Improved Intuitionistic Fuzzy C-Means Clustering Algorithm.
Abstract
Segmentation plays an important role in the field of image processing and computer vision. Intuitionistic fuzzy C-means (IFCM) clustering algorithm emerged as an effective technique for image segmentation in recent years. However, standard fuzzy C-means (FCM) and IFCM algorithms are sensitive to noise and initial cluster centers, and they ignore the spatial relationship of pixels. In view of these shortcomings, an improved algorithm based on IFCM is proposed in this paper. Firstly, we propose a modified non-membership function to generate intuitionistic fuzzy set and a method of determining initial clustering centers based on grayscale features, they highlight the effect of uncertainty in intuitionistic fuzzy set and improve the robustness to noise. Secondly, an improved nonlinear kernel function is proposed to map data into kernel space to measure the distance between data and the cluster centers more accurately. Thirdly, the local spatial-gray information measure is introduced, which considers membership degree, gray features and spatial position information at the same time. Finally, we propose a new measure of intuitionistic fuzzy entropy, it takes into account fuzziness and intuition of intuitionistic fuzzy set. The experimental results show that compared with other IFCM based algorithms, the proposed algorithm has better segmentation and clustering performance.
Year
DOI
Venue
2019
10.3837/tiis.2019.06.020
KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS
Keywords
Field
DocType
Image segmentation,intuitionistic fuzzy set,fuzzy theory,C-means clustering
Pattern recognition,Computer science,Fuzzy logic,Image segmentation,Artificial intelligence,Cluster analysis,Distributed computing
Journal
Volume
Issue
ISSN
13
6
1976-7277
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Jun Kong111118.94
Jian Hou212617.11
Min Jiang33913.65
Jin-Hua Sun4122.70