  Minimisation of discrete energies defined over factors is an important
problem in computer vision, and a vast number of MAP inference algorithms have
been proposed. Different inference algorithms perform better on factor graph
models (GMs) from different underlying problem classes, and in general it is
difficult to know which algorithm will yield the lowest energy for a given GM.
To mitigate this difficulty, survey papers advise the practitioner on what
algorithms perform well on what classes of models. We take the next step
forward, and present a technique to automatically select the best inference
algorithm for an input GM. We validate our method experimentally on an extended
version of the OpenGM2 benchmark, containing a diverse set of vision problems.
On average, our method selects an inference algorithm yielding labellings with
96% of variables the same as the best available algorithm.
