Evolving descriptors for texture segmentation

Authors:
Christophe Jacquelin;André Aurengo;Gilles Hejblum
Affiliations:
Unitéde Recherches en Imagerie Biomédicale Morphologique et Fonctionnelle, INSERM U66, CHU Pitié-Salpêtrière, 91 bd de l'Hopital, 75634 Paris Cedex 13, France;Unitéde Recherches en Imagerie Biomédicale Morphologique et Fonctionnelle, INSERM U66, CHU Pitié-Salpêtrière, 91 bd de l'Hopital, 75634 Paris Cedex 13, France;Unitéde Recherches en Imagerie Biomédicale Morphologique et Fonctionnelle, INSERM U66, CHU Pitié-Salpêtrière, 91 bd de l'Hopital, 75634 Paris Cedex 13, France
Venue:
Pattern Recognition
Year:
1997

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Abstract

A new method for texture segmentation is proposed. In this supervised method, texture descriptors are based on grey level co-occurrences. A texture descriptor corresponds to an individual in a population, and a population corresponds to a given texture class. A genetic algorithm generates several distinct and efficient individuals adapted to discriminate proposed textures. Then, the individuals compete for territories in an image composed of samples derived from the learned textures. At the end of the simulated evolution, population territories match the texture regions of the image. The method provides excellent results on various examples of synthetic and natural textures.