DPA: a deterministic approach to the MAP problem

Author

Berthod, Marc ; Kato, Zoltan ; Zerubia, Josiane

Author_Institution

Inst. Nat. de Recherche en Inf. et Autom., Sophia Antipolis, France

Volume

4

Issue

9

fYear

1995

fDate

9/1/1995 12:00:00 AM

Firstpage

1312

Lastpage

1314

Abstract

Deterministic pseudo-annealing (DPA) is a new deterministic optimization method for finding the maximum a posteriori (MAP) labeling in a Markov random field, in which the probability of a tentative labeling is extended to a merit function on continuous labelings. This function is made convex by changing its definition domain. This unambiguous maximization problem is solved, and the solution is followed down to the original domain, yielding a good, if suboptimal, solution to the original labeling assignment problem. The performance of DPA is analyzed on randomly weighted graphs

Keywords

Markov processes; graph theory; image processing; maximum likelihood estimation; MAP problem; Markov random field; continuous labelings; convex function; deterministic approach; deterministic optimization method; deterministic pseudo-annealing; image processing; labeling assignment problem; maximization problem; maximum a posteriori labeling; merit function; performance; randomly weighted graphs; suboptimal solution; Dynamic programming; Image classification; Image processing; Image restoration; Labeling; Markov random fields; Optimization methods; Performance analysis; Samarium; Simulated annealing;

fLanguage

English

Journal_Title

Image Processing, IEEE Transactions on

Publisher

ieee

ISSN

1057-7149

Type

jour

DOI

10.1109/83.413175

Filename

413175