Hierarchical Multiple Markov Chain Model for Unsupervised Texture Segmentation

Abstract : In this work, we present a novel multiscale texture model, and a related algorithm for the unsupervised segmentation of color images. Elementary textures are characterized by their spatial interactions with neighboring regions along selected directions. Such interactions are modeled in turn by means of a set of Markov chains, one for each direction, whose parameters are collected in a feature vector that synthetically describes the texture. Based on the feature vectors, the texture are then recursively merged, giving rise to larger and more complex textures, which appear at different scales of observation: accordingly, the model is named Hierarchical Multiple Markov Chain (H-MMC). The Texture Fragmentation and Reconstruction (TFR) algorithm, addresses the unsupervised segmen- tation problem based on the H-MMC model. The “fragmentation” step allows one to find the elementary textures of the model, while the “reconstruction” step defines the hierarchical image segmentation based on a probabilistic measure (texture score) which takes into account both region scale and inter-region interactions. The performance of the proposed method was assessed through the Prague segmentation benchmark, based on mosaics of real natural textures, and also tested on real-world natural and remote sensing images.
Type de document :
Article dans une revue
IEEE Trans. on Image Processing, IEEE, 2009, 18 (8), pp.1830-1843
Liste complète des métadonnées

Contributeur : Raffaele Gaetano <>
Soumis le : vendredi 16 juillet 2010 - 18:33:54
Dernière modification le : mercredi 15 septembre 2010 - 15:08:50
Document(s) archivé(s) le : vendredi 22 octobre 2010 - 15:16:02


Fichiers produits par l'(les) auteur(s)


  • HAL Id : inria-00503201, version 1



G. Scarpa, R. Gaetano, M. Haindl, J. Zerubia. Hierarchical Multiple Markov Chain Model for Unsupervised Texture Segmentation. IEEE Trans. on Image Processing, IEEE, 2009, 18 (8), pp.1830-1843. <inria-00503201>



Consultations de
la notice


Téléchargements du document