In this paper, a new learning algorithm is proposed with the purpose of tex
ture segmentation. The algorithm is a competitive clustering scheme with tw
o specific features: elliptical clustering is accomplished by incorporating
the Mahalanobis distance measure into the learning rules and under-utiliza
tion of smaller clusters is avoided by incorporating a frequency-sensitive
term. In the paper, an efficient learning rule that incorporates these feat
ures is elaborated. In the experimental section, several experiments demons
trate the usefulness of the proposed technique for the segmentation of text
ured images. On the compositions of textured images, Gabor filters were app
lied to generate texture features. The segmentation performance is compared
to k-means clustering with and without the use of the Mahalanobis distance
and to the ordinary competitive learning scheme. It is demonstrated that t
he proposed algorithm outperforms the others. A fuzzy version of the techni
que is introduced, and experimentally compared with fuzzy versions of the k
-means and competitive clustering algorithms. The same conclusions as for t
he hard clustering case hold. (C) 2001 Elsevier Science B.V. All rights res
erved.