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.