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Labeling Irregular Graphs with Belief Propagation

  • Ifeoma Nwogu
  • Jason J. Corso
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4958)

Abstract

This paper proposes a statistical approach to labeling images using a more natural graphical structure than the pixel grid (or some uniform derivation of it such as square patches of pixels). Typically, low-level vision estimations based on graphical models work on the regular pixel lattice (with a known clique structure and neighborhood). We move away from this regular lattice to more meaningful statistics on which the graphical model, specifically the Markov network is defined. We create the irregular graph based on superpixels, which results in significantly fewer nodes and more natural neighborhood relationships between the nodes of the graph. Superpixels are a local, coherent grouping of pixels which preserves most of the structure necessary for segmentation. Their use reduces the complexity of the inferences made from the graphs with little or no loss of accuracy. Belief propagation (BP) is then used to efficiently find a local maximum of the posterior probability for this Markov network. We apply this statistical inference to finding (labeling) documents in a cluttered room (under moderately different lighting conditions).

Keywords

Gaussian Mixture Model Belief Propagation Markov Random Field Synthetic Image Label Image 
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 2008

Authors and Affiliations

  • Ifeoma Nwogu
    • 1
  • Jason J. Corso
    • 1
  1. 1.Department of Computer Science and EngineeringState University of New York at BuffaloBuffalo

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