A Markov random field model-based approach to unsupervised texture segmentation using local and global spatial statistics
- 1 June 1995
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Image Processing
- Vol. 4 (6) , 856-862
- https://doi.org/10.1109/83.388090
Abstract
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 correspondence, we present an unsupervised texture segmentation method that does not require 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 that enables dynamic creation of new regions during the optimization process. A Bayesian estimate of this map is computed using a deterministic relaxation algorithm. Results on real-world textured images are presented.Keywords
This publication has 10 references indexed in Scilit:
- Multifractals, texture, and image analysisPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Motion segmentation and qualitative dynamic scene analysis from an image sequenceInternational Journal of Computer Vision, 1993
- Gibbs Random Fields, Fuzzy Clustering, and the Unsupervised Segmentation of Textured ImagesCVGIP: Graphical Models and Image Processing, 1993
- Unsupervised segmentation of noisy and textured images using Markov random fieldsCVGIP: Graphical Models and Image Processing, 1992
- Unsupervised texture segmentation using Markov random field modelsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1991
- Boundary detection by constrained optimizationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1990
- Unsupervised segmentation of textured images by edge detection in multidimensional featurePublished by Institute of Electrical and Electronics Engineers (IEEE) ,1989
- Bayesian clustering for unsupervised estimation of surface and texture modelsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1988
- Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of ImagesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1984
- Statistical and structural approaches to textureProceedings of the IEEE, 1979