article · 1999

MRMRF Texture Classification and MCMC Parameter Estimation

Jun Liu, Lei Wang, Stan Z. Li

Cited by 0Abstract and details only

Abstract

Texture classification is an important area in the field of texture analysis. In this paper, we propose a novel stochastic approach--multiresolution Markov Random Field (MRMRF) model to represent textures and a parameter estimation method based on Markov chain Monte Carlo method is proposed. The parameters estimated from the decomposed subbands can be used as features to classify textures. The classifier used here is nearest linear combination (NLC) which uses the combination of the features of several prototypes of an original texture to fit the features of the query texture. This method is better than NN(nearest neighbor) classifier. The experiment results illustrate the effectiveness of our method. 1. Introduction Texture is one of the basic characteristics of a visible surface and it provides important information for scene interpretation as well as image processing. It plays a crucial role in computer vision and pattern recognition. Understanding texture is a great part in image...

Cite this paper

APA 7

Liu, J., Wang, L., & Li, S. Z. (1999). MRMRF Texture Classification and MCMC Parameter Estimation.

MLA 9

Liu, Jun, et al. “MRMRF Texture Classification and MCMC Parameter Estimation.” 1999.

Chicago

Liu, Jun, Lei Wang, and Stan Z. Li. 1999. “MRMRF Texture Classification and MCMC Parameter Estimation.”

Harvard

Liu, J., Wang, L. and Li, S.Z. (1999) ‘MRMRF Texture Classification and MCMC Parameter Estimation’.

BibTeX

@article{liu1999mrmrf,
  title = {{MRMRF Texture Classification and MCMC Parameter Estimation}},
  author = {Liu, Jun and Wang, Lei and Li, Stan Z.},
  year = {1999}
}

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