HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation

A markov random field model-based approach to unsupervised texture segmentation using local and global spatial statistics

Charles Kervrann 1 Fabrice Heitz 1
1 TEMIS - Advanced Image Sequence Processing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, INRIA Rennes
Abstract : The general problem of unsupervised textured image segmentation remains a fundamental but not entirely solved issue in image analysis. Many studies have proven that statistical model-based texture segmentation algorithms yield good results provided that the model parameters and the number of regions be known a priori. In this paper, we present an unsupervised texture segmentation method which does not require a priori knowledge about the different texture regions, their parameters or the number of available texture classes. The proposed algorithm relies on the analysis of local and global second and higher order spatial statistics of the original images. The segmentation map is modeled using an augmented-state Markov random field, including an outlier class which enables dynamic creation of new regions during the optimization process. A bayesian estimates of this map is computed using a deterministic relaxation algorithm. The whole segmentation procedure is controlled by one single parameter. Results on mosaics of natural textures and real-world textured images show the ability of the model to yield relevant and robust segmentations when the number of regions and the different texture classes are not known a priori.
Document type :
Complete list of metadata

Contributor : Rapport de Recherche Inria Connect in order to contact the contributor
Submitted on : Wednesday, May 24, 2006 - 3:53:47 PM
Last modification on : Friday, February 4, 2022 - 3:21:56 AM
Long-term archiving on: : Sunday, April 4, 2010 - 10:00:16 PM


  • HAL Id : inria-00074610, version 1


Charles Kervrann, Fabrice Heitz. A markov random field model-based approach to unsupervised texture segmentation using local and global spatial statistics. [Research Report] RR-2062, INRIA. 1993. ⟨inria-00074610⟩



Record views


Files downloads