Segmentation d'Images Texturées Couleur à l'aide de modèles paramétriques pour approche la distribution des erreurs de prédiction linéaires.

We propose novel a priori parametric models to approximate the distribution of the two dimensional multichannel linear prediction error in order to improve the performance of color texture segmentation algorithms. Two dimensional linear prediction models are used to characterize the spatial structures in color images. The multivariate linear prediction error of these texture models is approximated with Wishart distribution and multivariate Gaussian mixture models. A novel color texture segmentation framework based on these models and a spatial regularization model of initial class label fields is presented. For the proposed method and with different color spaces, experimental results show better performances in terms of percentage segmentation error, in comparison with the use of a multivariate Gaussian law.