Paper Info
Title
Intuitionistic Fuzzy Clustering with Applications in Computer Vision
Abstract
Intuitionistic fuzzy sets are generalized fuzzy sets whose elements are characterized by a membership, as well as a non-membership value. The membership value indicates the degree of belongingness, whereas the non-membership value indicates the degree of non-belongingness of an element to that set. The utility of intuitionistic fuzzy sets theory in computer vision is increasingly becoming apparent, especially as a means to coping with noise. In this paper, we investigate the issue of clustering intuitionistic fuzzy image representations. To achieve that we propose a clustering approach based on the fuzzy c-means algorithm utilizing a novel similarity metric defined over intuitionistic fuzzy sets. The performance of the proposed algorithm is evaluated for object clustering in the presence of noise and image segmentation. The results indicate that clustering intuitionistic fuzzy image representations can be more effective, noise tolerant and efficient as compared with the conventional fuzzy c-means clustering of both crisp and fuzzy image representations.
Year
DOI
Venue
2008
10.1007/978-3-540-88458-3_69
ACIVS
Keywords
Field
DocType
intuitionistic fuzzy sets theory,fuzzy set,intuitionistic fuzzy image representation,clustering approach,intuitionistic fuzzy set,intuitionistic fuzzy clustering,conventional fuzzy c-means,non-membership value,fuzzy c-means,computer vision,image segmentation,fuzzy image representation,fuzzy clustering
Fuzzy clustering,Computer vision,Fuzzy classification,Defuzzification,Pattern recognition,Computer science,Fuzzy set operations,Fuzzy set,Artificial intelligence,Type-2 fuzzy sets and systems,Fuzzy number,Membership function
Conference
Volume
ISSN
Citations 
5259
0302-9743
11
PageRank 
References 
Authors
0.63
14
4
Name
Order
Citations
PageRank
Dimitris K. Iakovidis123418.81
Nikos Pelekis288159.28
Evangelos E. Kotsifakos3854.98
Ioannis Kopanakis426416.68