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

[+] Author Affiliations
Luca Costantini

Fondazione Ugo Bordoni, Viale del Policlinico, 147, 00161 Rome, Italy

Licia Capodiferro

Fondazione Ugo Bordoni, Viale del Policlinico, 147, 00161 Rome, Italy

Marco Carli

Università degli Studi Roma Tre, Engineering Department, Via della Vasca Navale 84, 00146 Rome, Italy

Alessandro Neri

Università degli Studi Roma Tre, Engineering Department, Via della Vasca Navale 84, 00146 Rome, Italy

J. Electron. Imaging. 22(4), 043015 (Nov 12, 2013). doi:10.1117/1.JEI.22.4.043015
History: Received January 17, 2013; Revised August 27, 2013; Accepted September 18, 2013
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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.

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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", J. Electron. Imaging. 22(4), 043015 (Nov 12, 2013). ; http://dx.doi.org/10.1117/1.JEI.22.4.043015


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