Towards an unsupervised optimal fuzzy clustering algorithm for image database organization

Author

Xiong, Xuejian ; Chan, Kap Luk

Author_Institution

Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore

Volume

3

fYear

2000

fDate

2000

Firstpage

897

Abstract

In this paper, an unsupervised optimal fuzzy clustering (UOFC) algorithm is proposed for the classification of images. The main advantage of UOFC is that it can deal with clusters of arbitrary shape, as well as the ability to improve the convergence rate and optimality of the algorithm. In addition, the cluster validity criterion, which combines the properties of the fuzzy membership degrees and cluster geometrical properties, is used in the UOFC algorithm to evaluate the goodness of clustering. The UOFC algorithm is evaluated by simulation data as well as Brodatz textures, represented by the Gabor features

Keywords

convergence; file organisation; fuzzy set theory; image classification; optimisation; pattern clustering; visual databases; Brodatz textures; Gabor features; UOFC; cluster geometrical properties; cluster validity criterion; convergence rate; fuzzy membership degrees; image database organization; unsupervised optimal fuzzy clustering algorithm; Clustering algorithms; Data engineering; Fuzzy systems; Humans; Image databases; Inference algorithms; Phase change materials; Shape; Spatial databases;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 2000. Proceedings. 15th International Conference on

Conference_Location

Barcelona

ISSN

1051-4651

Print_ISBN

0-7695-0750-6

Type

conf

DOI

10.1109/ICPR.2000.903688

Filename

903688