L'ubor Ladický , ETH Zurich, Zurich
Chris Russell , University College London, London
Pushmeet Kohli , Microsoft Research, Cambridge
Philip H. S. Torr , Oxford Brookes University, Oxford
This paper makes two contributions: the first is the proposal of a new model - the associative hierarchical random field (AHRF), and a novel algorithm for its optimisation; the second is the application of this model to the problem of semantic segmentation. Most methods for semantic segmentation are formulated as a labelling problem for variables that might correspond to either pixels or segments such as super-pixels. It is well known that the generation of super pixel segmentations is not unique. This has motivated many researchers to use multiple super pixel segmentations for problems such as semantic segmentation or single view reconstruction. These super-pixels have not yet been combined in a principled manner, this is a difficult problem, as they may overlap, or be nested in such a way that the segmentations form a segmentation tree. Our new hierarchical random field model allows information from all of the multiple segmentations to contribute to a global energy. MAP inference in this model can be performed efficiently using powerful graph cut based move making algorithms.
Segmentation, Conditional Random Fields, Discrete Energy Minimisation, Object Recognition
L. Ladický, C. Russell, P. Kohli and P. H. Torr, "Associative Hierarchical Random Fields," in IEEE Transactions on Pattern Analysis & Machine Intelligence.