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ICIAP
1999
ACM

Texture Segmentation by Frequency-Sensitive Elliptical Competitive Learning

10 years 1 months ago
Texture Segmentation by Frequency-Sensitive Elliptical Competitive Learning
In this paper a new learning algorithm is proposed with the purpose of texture segmentation. The algorithm is a competitive clustering scheme with two specific features: elliptical clustering is accomplished by incorporating the Mahalanobis distance measure into the learning rules, and underutilization of smaller clusters is avoided by incorporating a frequency-sensitive term. In the paper, an efficient learning rule that incorporates these features is elaborated. In the experimental section, several experiments demonstrate the usefulness of the proposed technique for the segmentation of textured images. On compositions of textured images, Gabor filters were applied 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 the proposed algorithm outperforms the others.
Steve De Backer, Paul Scheunders
Added 04 Aug 2010
Updated 04 Aug 2010
Type Conference
Year 1999
Where ICIAP
Authors Steve De Backer, Paul Scheunders
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