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Proceedings Paper

Markov random field texture models for classification
Author(s): Roman Antosik; David R. Scott; Gerald M. Flachs
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Paper Abstract

Two novel approaches to texture classification based upon stochastic modeling using Markov Random Fields are presented and contrasted. The first approach uses a clique-based probabilistic neighborhood structure and Gibbs distribution to derive the quasi likelihood estimates of the model coefficients. Likelihood ratio tests formed by the quasi-likelihood functions of pairs of textures are evaluated in the decision strategy to classify texture samples. The second approach uses a least squares prediction error model and error signature analysis to model and classify textures. The distribution of the errors is the information used in the decision algorithm which employs K-nearest neighbors techniques. A new statistic and complexity measure are introduced called the Knearest neighbor statistic (KNS) and complexity (KNC) which measure the overlap in K-nearest neighbor conditional distributions. Parameter vectors for each model, neighborhood size and structure, performance of the maximum likelihood and K-nearest neighbor decision strategies are presented and interesting results discussed. Results from classifying real video pictures of six cloth textures are presented and analyzed.

Paper Details

Date Published: 1 November 1990
PDF: 10 pages
Proc. SPIE 1301, Digital Image Processing and Visual Communications Technologies in the Earth and Atmospheric Sciences, (1 November 1990); doi: 10.1117/12.21414
Show Author Affiliations
Roman Antosik, New Mexico State Univ. (United States)
David R. Scott, New Mexico State Univ. (United States)
Gerald M. Flachs, New Mexico State Univ. (United States)

Published in SPIE Proceedings Vol. 1301:
Digital Image Processing and Visual Communications Technologies in the Earth and Atmospheric Sciences
Paul Janota, Editor(s)

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