Topology and parameter estimation in Markov random field modeling

Xavier Descombes; Francoise J. Preteux

doi:10.1117/12.162034

29 October 1993 Topology and parameter estimation in Markov random field modeling

Xavier Descombes, Francoise J. Preteux

Proceedings Volume 2032, Neural and Stochastic Methods in Image and Signal Processing II; (1993) https://doi.org/10.1117/12.162034
Event: SPIE's 1993 International Symposium on Optics, Imaging, and Instrumentation, 1993, San Diego, CA, United States

Abstract

Within the framework of pattern recognition via Markov random field modelling, we propose three methods for estimating the topological and statistical parameters characterizing the model, namely clique orders, anisotropy indices, weighting coefficient between cliques with various orders, coefficients of polynomial potential functions and temperature. The developed approaches successively exploit local information associated with conditional probability distributions, a similarity criterion expressed as a distance in variations between appropriate probability distributions, standard least-square estimation and renormalization theory. Extensive experiments performed on a variety of synthetic images have established the relevance and accuracy of the proposed method. Its performances are further demonstrated within the framework of urban areas segmentation in SPOT images.

Citation Download Citation

Xavier Descombes and Francoise J. Preteux "Topology and parameter estimation in Markov random field modeling", Proc. SPIE 2032, Neural and Stochastic Methods in Image and Signal Processing II, (29 October 1993); https://doi.org/10.1117/12.162034

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