Paper Info
Title
A Segmentation Method For Textured Images Based On The Maximum Posterior Mode Criterion
Abstract
We consider the problem of semi-supervised segmentation of textured images. Recently, reweighted belief propagation has been introduced as a solution for Bayesian inference with respect to the maximum posterior mode criterion. In this paper, we show how to adapt reweighted belief propagation to the problem of segmentation of textured images. An adaptive parameter estimation technique is also provided. Then, we compare classical simulated annealing with the recently introduced reweighted belief propagation algorithm, in terms of segmentation results.
Year
DOI
Venue
2011
10.1109/ICASSP.2011.5946737
2011 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING
Keywords
Field
DocType
Texture segmentation, Markov random field, Gauss-Markov random field, graphical models, simulated annealing, reweighted belief-propagation
Simulated annealing,Scale-space segmentation,Bayesian inference,Pattern recognition,Computer science,Segmentation,Markov random field,Image segmentation,Artificial intelligence,Estimation theory,Belief propagation
Conference
ISSN
Citations 
PageRank 
1520-6149
0
0.34
References 
Authors
3
1
Name
Order
Citations
PageRank
Frédéric Lehmann153.17