Automatically Selecting Inference Algorithms for Discrete Energy Minimisation

Paul Henderson, Vittorio Ferrari

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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 [1–3] 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 [3], 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.
Original languageEnglish
Title of host publicationComputer Vision -- ECCV 2016
Subtitle of host publication14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part V
EditorsBastian Leibe, Jiri Matas, Nicu Sebe, Max Welling
Place of PublicationCham
PublisherSpringer International Publishing
Number of pages18
ISBN (Electronic)978-3-319-46454-1
ISBN (Print)978-3-319-46453-4
Publication statusPublished - 16 Sep 2016
Event14th European Conference on Computer Vision 2016 - Amsterdam, Netherlands
Duration: 8 Oct 201616 Oct 2016

Publication series

NameLecture Notes in Computer Science
PublisherSpringer International Publishing
ISSN (Print)0302-9743


Conference14th European Conference on Computer Vision 2016
Abbreviated titleECCV 2016
Internet address

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