Title
Compound Markov Random Field Model of Signals on Graph: An Application to Graph Learning
Abstract
In this work we address the problem of Signal on Graph (SoG) modeling, which can provide a powerful image processing tool for suitable SoG construction. We propose a novel SoG Markovian model suited to jointly characterizing the graph signal values and the graph edge processes. Specifically, we resort to the compound MRF called pixel-edge model formerly introduced in natural images modeling and we reformulate it to frame SoG modeling. We derive the Maximum A Posteriori Laplacian estimator associated to the compound MRF, and we show that it encompasses simpler state-of-the-art estimators for proper parameter settings. Numerical simulations show that the Maximum A Priori Laplacian estimator based on the proposed model outperforms state-of-the-art competitors under different respects. The Spectral Graph Wavelet Transform basis associated to the Maximum A Priori Laplacian estimation guarantees excellent compression of the given SoG. These results show that the compound MRF represents a powerful theoretical tool to characterize the strong and rich interactions that can be found between the signal values and the graph structures, and pave the way to its application to various SoG problems.
Year
DOI
Venue
2018
10.1109/EUVIP.2018.8611758
2018 7th European Workshop on Visual Information Processing (EUVIP)
Keywords
Field
DocType
natural images,Maximum A Posteriori Laplacian estimator,compound MRF,Maximum A Priori Laplacian estimator,Spectral Graph Wavelet Transform basis,Graph learning,novel SoG Markovian model,graph signal values,graph edge processes,pixel-edge model
Markov process,Markov random field,Computer science,A priori and a posteriori,Image processing,Algorithm,Graph Edge,Maximum a posteriori estimation,Wavelet transform,Estimator
Conference
ISSN
ISBN
Citations 
2164-974X
978-1-5386-6898-6
0
PageRank 
References 
Authors
0.34
4
4
Name
Order
Citations
PageRank
Stefania Colonnese113726.43
Giulio Pagliari200.34
Mauro Biagi315826.03
Roberto Cusani416833.10