Optimizing Binary MRFs with Higher Order Cliques

Authors:
Asem M. Ali;Aly A. Farag;Georgy L. Gimel'Farb
Affiliations:
Computer Vision and Image Processing Laboratory, University of Louisville, USA;Computer Vision and Image Processing Laboratory, University of Louisville, USA;Department of Computer Science, The University of Auckland, New Zealand
Venue:
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Year:
2008

Citing 0
Cited 6

Labelling color images by modelling the colors density using a linear combination of Gaussians and EM algorithm

ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Minimizing count-based high order terms in markov random fields

EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
A hybrid approach for MRF optimization problems: Combination of stochastic sampling and deterministic algorithms

Computer Vision and Image Understanding
Generalized roof duality and bisubmodular functions

Discrete Applied Mathematics
Window annealing for pixel-labeling problems

Computer Vision and Image Understanding
Markov Random Field modeling, inference & learning in computer vision & image understanding: A survey

Computer Vision and Image Understanding

Quantified Score

Hi-index	0.00

Visualization

Abstract

Widespread use of efficient and successful solutions of Computer Vision problems based on pairwise Markov Random Field (MRF) models raises a question: does any link exist between the pairwise and higher order MRFs such that the like solutions can be applied to the latter models? This work explores such a link for binary MRFs that allow us to represent Gibbs energy of signal interaction with a polynomial function. We show how a higher order polynomial can be efficiently transformed into a quadratic function. Then energy minimization tools for the pairwise MRF models can be easily applied to the higher order counterparts. Also, we propose a method to analytically estimate the potential parameter of the asymmetric Potts prior. The proposed framework demonstrates very promising experimental results of image segmentation and can be used to solve other Computer Vision problems.