A generalized Swendsen-Wang algorithm for Bayesian nonparametric joint segmentation of multiple images

Jessica Sodjo; Audrey Giremus; Nicolas Dobigeon; Jean-François Giovannelli

ICASSP 2017accepted0 citations

A generalized Swendsen-Wang algorithm for Bayesian nonparametric joint segmentation of multiple images

Jessica Sodjo, Audrey Giremus, Nicolas Dobigeon, Jean-François Giovannelli

Abstract

A generalized Swendsen-Wang (GSW) algorithm is proposed for the joint segmentation of a set of multiple images sharing, in part, an unknown number of common classes. The class labels are a priori modeled by a combination of the hierarchical Dirichlet process (HDP) and the Potts model. The HDP allows the number of regions in each image and classes to be automatically inferred while the Potts model ensures spatially consistent segmentations. Compared to a classical Gibbs sampler, the GSW ensures a better exploration of the posterior distribution of the labels. To avoid label switching issues, the best partition is estimated using the Dahl's criterion.

BibTeX

@inproceedings{icassp2017_ageneralizedswen,
  title = {A generalized Swendsen-Wang algorithm for Bayesian nonparametric joint segmentation of multiple images},
  author = {Jessica Sodjo and Audrey Giremus and Nicolas Dobigeon and Jean-François Giovannelli},
  booktitle = {ICASSP 2017},
  year = {2017}
}