Joint segmentation of multiple images with shared classes: a Bayesian nonparametrics approach

Jessica Sodjo (1) , Audrey Giremus (1) , François Caron (2) , Jean-François Giovannelli (1) , Nicolas Dobigeon (3, 4)

1 IMS - Laboratoire de l'intégration, du matériau au système
2 University of Oxford
3 IRIT-SC - Signal et Communications
4 Toulouse INP - Institut National Polytechnique (Toulouse)

A combination of the hierarchical Dirichlet process (HDP) and the Potts model is proposed for the joint segmentation/classification of a set of images with shared classes. Images are first divided into homogeneous regions that are assumed to belong to the same class when sharing common characteristics. Simultaneously, the Potts model favors configurations defined by neighboring pixels belonging to the same class. This HDP-Potts model is elected as a prior for the images, which allows the best number of classes to be selected automatically. A Gibbs sampler is then designed to approximate the Bayesian estimators, under a maximum a posteriori (MAP) paradigm. Preliminary experimental results are finally reported using a set of synthetic images.

Mots clés: Hierarchical Dirichlet process; Potts model; Image; Segmentation; Bayesian nonparametrics

Domaines: Synthèse d'image et réalité virtuelle [cs.GR]; Traitement du signal et de l'image [eess.SP]; Traitement des images [eess.IV]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Intelligence artificielle [cs.AI]

Jessica Sodjo, Audrey Giremus, François Caron, Jean-François Giovannelli, Nicolas Dobigeon. Joint segmentation of multiple images with shared classes: a Bayesian nonparametrics approach. IEEE Workshop on statistical signal processing (SSP 2016), Jun 2016, Palma de Mallorca, Spain. pp.1-5, ⟨10.1109/SSP.2016.7551735⟩. ⟨hal-01695100v2⟩