Statistical Learning Procedure in Loopy Belief Propagation for Probabilistic Image Processing

Author

Tanaka, Kazuyuki

Author_Institution

Graduate Sch. of Inf. Sci., Tohoku Univ., Sendai

Volume

2

fYear

2005

fDate

28-30 Nov. 2005

Firstpage

741

Lastpage

746

Abstract

We give a fast and practical algorithm for statistical learning hyperparameters from observable data in probabilistic image processing, which is based on Gaussian graphical model and maximum likelihood estimation. Although hyperparameters in the probabilistic model are determined so as to maximize a marginal likelihood, a practical algorithm is described for the EM algorithm with the loopy belief propagation which is one of approximate inference algorithms in artificial intelligence

Keywords

Gaussian processes; image restoration; maximum likelihood estimation; probability; EM algorithm; Gaussian graphical model; approximate inference algorithm; artificial intelligence; loopy belief propagation; marginal likelihood; maximum likelihood estimation; probabilistic image processing; statistical learning procedure; Belief propagation; Degradation; Graphical models; Image processing; Image restoration; Inference algorithms; Lattices; Maximum likelihood estimation; Pixel; Statistical learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on

Conference_Location

Vienna

Print_ISBN

0-7695-2504-0

Type

conf

DOI

10.1109/CIMCA.2005.1631557

Filename

1631557