Neighborhood-consensus message passing as a framework for generalized iterated conditional expectations

Authors:
Tijana Ruić;Aleksandra Piurica;Wilfried Philips
Affiliations:
Department for Telecommunications and Information Processing (TELIN), Ghent University, Sint-Pietersnieuwstraat 41, B-9000 Gent, Belgium;Department for Telecommunications and Information Processing (TELIN), Ghent University, Sint-Pietersnieuwstraat 41, B-9000 Gent, Belgium;Department for Telecommunications and Information Processing (TELIN), Ghent University, Sint-Pietersnieuwstraat 41, B-9000 Gent, Belgium
Venue:
Pattern Recognition Letters
Year:
2012

Citing 18
Cited 0

Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
Markov random field modeling in computer vision

Markov random field modeling in computer vision
A revolution: belief propagation in graphs with cycles

NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Learning Low-Level Vision

International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Fast Approximate Energy Minimization via Graph Cuts

IEEE Transactions on Pattern Analysis and Machine Intelligence
What Energy Functions Can Be Minimizedvia Graph Cuts?

IEEE Transactions on Pattern Analysis and Machine Intelligence
"GrabCut": interactive foreground extraction using iterated graph cuts

ACM SIGGRAPH 2004 Papers
Convergent Tree-Reweighted Message Passing for Energy Minimization

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors

IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning with structured sparsity

ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Exploiting structure in wavelet-based Bayesian compressive sensing

IEEE Transactions on Signal Processing
Model-based compressive sensing

IEEE Transactions on Information Theory
Single image example-based super-resolution using cross-scale patch matching and markov random field modelling

ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I
Loopy belief propagation for approximate inference: an empirical study

UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs

IEEE Transactions on Information Theory
MAP estimation via agreement on trees: message-passing and linear programming

IEEE Transactions on Information Theory
Wavelet-based image denoising using a Markov random field a priori model

IEEE Transactions on Image Processing
A joint inter- and intrascale statistical model for Bayesian wavelet based image denoising

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.10

Visualization

Abstract

In this paper we propose a novel inference method for maximum a posteriori estimation with Markov random field prior. The central idea is to integrate a kind of joint ''voting'' of neighboring labels into a message passing scheme similar to loopy belief propagation (LBP). While the LBP operates with many pairwise interactions, we formulate ''messages'' sent from a neighborhood as a whole. Hence the name neighborhood-consensus message passing (NCMP). The practical algorithm is much simpler than LBP and combines the flexibility of iterated conditional modes (ICM) with some ideas of more general message passing. The proposed method is also a generalization of the iterated conditional expectations algorithm (ICE): we revisit ICE and redefine it in a message passing framework in a more general form. We also develop a simplified version of NCMP, called weighted iterated conditional modes (WICM), that is suitable for large neighborhoods. We verify the potentials of our methods on four different benchmarks, showing the improvement in quality and/or speed over related inference techniques.