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Parameter-estimation in the auto-binomial model using the coding-and pseudo-likelihood method approached with simulated annealing and numerical optimization
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE).
2001 (English)In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 22, no 11, p. 1233-1246Article in journal (Refereed) Published
Abstract [en]

In texture analysis, the Gibbs sampler constitutes an important tool in the generation of synthetic textures. The textures are modeled as distributions with specified parameters. In this paper, we study the estimation process of the parameters in such distributions and compare Besags coding method with a pseudo-likelihood method. We also compare simulated annealing with the Newton-Raphson method to find the global maximum of a likelihood or pseudo-likelihood function. For some textures, the two methods differ but in most case there are no important differences between them. The two maximization methods find the same maximum, but the Newton-Raphson method is much faster. However, the Newton-Raphson method cannot be applied in some cases when the location of the maximum differs too much from the starting points. Here, it is often possible to find the global maximum using simulated annealing. The methods have been used in an application with newsprint.

Place, publisher, year, edition, pages
Amsterdam: Elsevier , 2001. Vol. 22, no 11, p. 1233-1246
Keywords [en]
Estimation, Annealing, Encoding (symbols), Optimization, Textures
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:hh:diva-3432DOI: 10.1016/S0167-8655(01)00055-1ISI: 000170125300006Scopus ID: 2-s2.0-0035452212OAI: oai:DiVA.org:hh-3432DiVA, id: diva2:295185
Available from: 2010-02-17 Created: 2009-12-01 Last updated: 2018-03-23Bibliographically approved

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Johansson, Jan-Olof

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
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  • Other style
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  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
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  • sv-SE
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