Texture-based segmentation using markov random field models and approximate Bayesian estimators based on trees
- 31 Downloads
We describe segmentation based on textures using the label and image model of D. Gemanet al., “Boundary Detection by Constrained Optimization,”IEEE Trans. Pattern Analysis and Machine Intelligence, 12(7):609–628, July 1990. We replace their maximuma posteriori estimation criterion with a Bayesian estimator that minimizes the sum of the pixel misclassification probabilities. The new estimation goal allows the use of a different computational algorithm, which is deterministic rather than random, based on approximating lattices by trees. An example demonstrating an accurate segmentation of a collage of Brodatz textures is included.
KeywordsImage textures Image segmentation Markov random fields Kolmogorov-Smirnov statistic
Unable to display preview. Download preview PDF.
- 1.Julian Besag. On the statistical analysis of dirty pictures.J. Royal Stat. Soc. B, 48:259–302, 1986.Google Scholar
- 2.Donald Geman, Stuart Geman, Christine Graffigne, and Ping Dong. Boundary detection by constrained optimization.IEEE Trans. PAMI, 12(7):609–627, July 1990.Google Scholar
- 3.Stuart Geman and Donald Geman. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images.IEEE Trans. PAMI, 6(6):721–741, November 1984.Google Scholar
- 4.Christine Graffigne.Experiments in Texture Analysis and Segmentation. PhD thesis, Brown University, Providence, RI, May 1987.Google Scholar
- 5.J. Marroquin, S. Mitter, and T. Poggio. Probabilistic solution of ill—posed problems in computational vision.J. Am. Stat. Assoc., 82(397):76–89, 1987.Google Scholar
- 6.J. M. Ortega and W. C. Rheinboldt.Iterative Solution of Nonlinear Equations in Several Variables. Academic Press, Inc., San Diego, 1970.Google Scholar
- 7.William H. Press, Brian P. Flannery, Saul A. Teukolsky, and William T. Vetterling.Numerical Recipes in C: The Art of Scientific Computing. Cambridge Univ. Press, Cambridge, 2 edition, 1992.Google Scholar
- 8.Chi-hsin Wu.Deterministic Parallelizable Solutions for Bayesian Markov Random Field Estimation Problems. PhD thesis, Purdue University, West Lafayette, IN, USA, May 1994.Google Scholar
- 9.Chi-hsin Wu and Peter C. Doerschuk. Tree approximations to Markov random fields.IEEE Trans. PAMI, 17(4):391–402, April 1995.Google Scholar