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Fast Generalized Belief Propagation for MAP Estimation on 2D and 3D Grid-Like
 

Summary: Fast Generalized Belief Propagation for
MAP Estimation on 2D and 3D Grid-Like
Markov Random Fields
Kersten Petersen, Janis Fehr, and Hans Burkhardt
Albert-Ludwigs-Universit¨at Freiburg, Institut f¨ur Informatik,
Lehrstuhl f¨ur Mustererkennung und Bildverarbeitung,
Georges-Koehler-Allee Geb. 052, 79110 Freiburg, Deutschland
petersep@informatik.uni-freiburg.de
Abstract. In this paper, we present two novel speed-up techniques for
deterministic inference on Markov random fields (MRF) via generalized
belief propagation (GBP). Both methods require the MRF to have a
grid-like graph structure, as it is generally encountered in 2D and 3D
image processing applications, e.g. in image filtering, restoration or seg-
mentation. First, we propose a caching method that significantly reduces
the number of multiplications during GBP inference. And second, we in-
troduce a speed-up for computing the MAP estimate of GBP cluster
messages by presorting its factors and limiting the number of possible
combinations. Experimental results suggest that the first technique im-
proves the GBP complexity by roughly factor 10, whereas the accelera-
tion for the second technique is linear in the number of possible labels.

  

Source: Albert-Ludwigs-Universität Freiburg, Institut für Informatik,, Lehrstuhls für Mustererkennung und Bildverarbeitung

 

Collections: Computer Technologies and Information Sciences