Markov Random Fields with Asymmetric Interactions for Modelling Spatial Context in Structured Scene Labelling

In this paper we propose a Markov random field with asymmetric Markov parameters to model the spatial and topological relationships between objects in structured scenes. The field is formulated in terms of conditional probabilities learnt 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. The incorporation of spatial information is shown to improve greatly the performance of some trivial classifiers.