Title :
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
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;
Conference_Titel :
Pattern Recognition, 2000. Proceedings. 15th International Conference on
Conference_Location :
Barcelona
Print_ISBN :
0-7695-0750-6
DOI :
10.1109/ICPR.2000.903688
