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1 November 1990 Markov random field texture models for classification
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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.
© (1990) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Roman Antosik, David R. Scott, and Gerald M. Flachs "Markov random field texture models for classification", Proc. SPIE 1301, Digital Image Processing and Visual Communications Technologies in the Earth and Atmospheric Sciences, (1 November 1990);


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