Automatically selecting inference algorithms for discrete energy minimisation
This addresses the challenge for practitioners in computer vision who need to choose among many inference algorithms for factor graph models, though it is incremental as it builds on existing survey advice.
The paper tackles the problem of automatically selecting the best inference algorithm for discrete energy minimization in computer vision, achieving results where the selected algorithm yields labellings with 96% of variables matching the best available algorithm.
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.