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Pattern Recognition and Image Analysis

, Volume 20, Issue 3, pp 324–334 | Cite as

Variational segmentation algorithms with label frequency constraints

Representation, Processing, Analysis, and Understanding of Images

Abstract

We consider image and signal segmentation problems within the Markov random field (MRF) approach and try to take into account label frequency constraints. Incorporating these constraints into MRF leads to an NP-hard optimization problem. For solving this problem we present a two-step approximation scheme that allows one to use hard, interval and soft constraints on label frequencies. On the first step a factorized approximation of the joint distribution is made (only local terms are included) and then, on the second step, the labeling is found by conditional maximization of the factorized joint distribution. The latter task is reduced to an easy-to-solve transportation problem. Basing on the proposed two-step approximation scheme we derive the ELM algorithm for tuning MRF parameters. We show the efficiency of our approach on toy signals and on the task of automated segmentation of Google Maps.

Key words

image segmentation signal segmentation Markov random fields hidden Markov models discrete optimization area prior variational inference linear programming 

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Copyright information

© Pleiades Publishing, Ltd. 2010

Authors and Affiliations

  1. 1.Dorodnicyn Computing Centre of the Russian Academy of SciencesMoscowRussia
  2. 2.CMC DepartmentLomonosov Moscow State UniversityMoscowRussia

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