Non-Gibbsian Markov Random Field for modelling spatial relationships in structured scenes.

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 hand-segmented images of buildings.

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