This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

Search for Publication

Year(s) from:  to 
Keywords (separated by spaces):

Strong Markov Random Field Model

R. Paget
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vol. 26, No. 3, pp. 408-413, March 2004


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.

Download in pdf format
  author = {R. Paget},
  title = {Strong Markov Random Field Model},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year = {2004},
  month = {March},
  pages = {408-413},
  volume = {26},
  number = {3},
  keywords = {Markov processes, Contingency table analysis, Nonparametric statistics, Texture, Model development}