Texture segmentation based on Laguerre Gauss functions and k-means algorithm driven by Kullback–Leibler divergence

Luca Costantini; Licia Capodiferro; Marco Carli; Alessandro Neri

doi:10.1117/1.JEI.22.4.043015

12 November 2013 Texture segmentation based on Laguerre Gauss functions and k-means algorithm driven by Kullback–Leibler divergence

Luca Costantini, Licia Capodiferro, Marco Carli, Alessandro Neri

Author Affiliations +

Journal of Electronic Imaging, Vol. 22, Issue 4, 043015 (November 2013). https://doi.org/10.1117/1.JEI.22.4.043015

Abstract

A new technique for texture segmentation is presented. The method is based on the use of Laguerre Gauss (LG) functions, which allow an efficient representation of textures. In particular, the marginal densities of the LG expansion coefficients are approximated by the generalized Gaussian densities, which are completely described by two parameters. The classification and the segmentation steps are performed by using a modified k -means algorithm exploiting the Kullback–Leibler divergence as similarity metric. This clustering method is a more efficient system for texture comparison, thus resulting in a more accurate segmentation. The effectiveness of the proposed method is evaluated by using mosaic image sets created by using the Brodatz dataset, and real images.

Citation Download Citation

Luca Costantini, Licia Capodiferro, Marco Carli, and Alessandro Neri "Texture segmentation based on Laguerre Gauss functions and k-means algorithm driven by Kullback–Leibler divergence," Journal of Electronic Imaging 22(4), 043015 (12 November 2013). https://doi.org/10.1117/1.JEI.22.4.043015

Published: 12 November 2013

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