Strong Markov Random Field Model
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Abstract—The strong Markov random field (strong-MRF) model is a submodel of the more general MRF-Gibbs model. The strong-MRF model defines a system whose field is Markovian with respect to a defined neighborhood, and all subneighborhoods are also Markovian. A checkerboard pattern is a perfect example of a strong Markovian system. Although the strong Markovian system requires a more stringent assumption about the field, it does have some very nice mathematical properties. One mathematical property is the ability to define the strong-MRF model with respect to its marginal distributions over the cliques. Also, a direct equivalence to the Analysis-of-Variance (ANOVA) log-linear construction can be proven. From this proof, the general ANOVA log-linear construction formula is acquired.
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Index Terms:
Markov processes, contingency table analysis, nonparametric statistics, texture, model development.
Citation:
Rupert Paget, "Strong Markov Random Field Model," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 3, pp. 408-413, Mar. 2004, doi:10.1109/TPAMI.2004.1262338
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