By Topic

Strong Markov random field model

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
R. Paget ; Comput. Vision Group, Zurich, Switzerland

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.

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:26 ,  Issue: 3 )