Fast Fusion Moves for Multi-model Estimation

  • Andrew Delong
  • Olga Veksler
  • Yuri Boykov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7572)


We develop a fast, effective algorithm for minimizing a well-known objective function for robust multi-model estimation. Our work introduces a combinatorial step belonging to a family of powerful move-making methods like α-expansion and fusion. We also show that our subproblem can be quickly transformed into a comparatively small instance of minimum-weighted vertex-cover. In practice, these vertex-cover subproblems are almost always bipartite and can be solved exactly by specialized network flow algorithms. Experiments indicate that our approach achieves the robustness of methods like affinity propagation, whilst providing the speed of fast greedy heuristics.


Facility Location Facility Location Problem Model Penalty Fusion Move Biclique Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Andrew Delong
    • 1
  • Olga Veksler
    • 1
  • Yuri Boykov
    • 1
  1. 1.University of Western OntarioCanada

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