NDLI: Inference strategies for the smoothness parameter in the Potts model

Content Provider	IEEE Xplore Digital Library
Author	Gimenez, J. Frery, A.C. Flesia, A.G.
Copyright Year	2013
Description	Author affiliation: Famaf, Univ. Nac. de Cordoba, Cordoba, Argentina (Gimenez, J.; Flesia, A.G.) \|\| Univ. Fed. de Alagoas, Maceio, Brazil (Frery, A.C.)
Abstract	The Potts model is a commonplace in Bayesian image analysis since its introduction as a convenient image prior. It is able to describe the distribution of classes, yielding a regularization term in the cost function to be minimized in many classification problems. The simplest isotropic version depends on a scalar smoothness parameter; its value controls the relative influence of the regularization with respect to the data. This work analyzes the performance of two pseudolike-lihood estimation procedures of the smoothness parameter of the Potts model: the classical one, which employs the map of classes, and a new estimator based on the posterior distribution, which also incorporates the evidence provided by the observed data. Our simulation study shows that the combination of prior information and observation data gives accurate β estimations when true data is provided. We also discuss its influence in the classification results when comparing contextual ICM (Iterated Conditional Modes) classification experiments with multispectral optical imagery, estimating the scalar parameter β with our estimator and the classical one. Our experiment shows promising results, since ICM with our estimator is able to distinguish image features that the classical ICM does not.
Sponsorship	IEEE Geosci. Remote Sens. Soc.
Starting Page	2539
Ending Page	2542
File Size	848671
Page Count	4
File Format	PDF
ISBN	9781479911141
ISSN	21536996
DOI	10.1109/IGARSS.2013.6723339
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-21
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computational modeling Bayes methods Data models Mathematical model Analytical models Maximum likelihood estimation Potts model Bayesian image analysis inference
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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