Non-Gibbsian Markov random field models for contextual labelling of structured scenes
Daniel C. Heesch, Michael F. Petrou · 2007
In this paper we propose a non-Gibbsian Markov random field to model the spatial and topological relationships between objects in structured scenes. The field is formulated in terms of conditional probabilities learned from a set of training images. A locally consistent labelling of new scenes is achieved by relaxing the Markov random field directly using these conditional probabilities. We evaluate our model on a varied collection of several hundred handsegmented images of buildings. 1