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Journal of Electronic Imaging

Texture segmentation based on Laguerre Gauss functions and k-means algorithm driven by Kullback–Leibler divergence
Author(s): Luca Costantini; Licia Capodiferro; Marco Carli; Alessandro Neri
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Paper 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.

Paper Details

Date Published: 12 November 2013
PDF: 10 pages
J. Electron. Imaging. 22(4) 043015 doi: 10.1117/1.JEI.22.4.043015
Published in: Journal of Electronic Imaging Volume 22, Issue 4
Show Author Affiliations
Luca Costantini, Fondazione Ugo Bordoni (Italy)
Licia Capodiferro, Fondazione Ugo Bordoni (Italy)
Marco Carli, Univ. degli Studi di Roma Tre (Italy)
Alessandro Neri, Univ. degli Studi di Roma Tre (Italy)


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