Markov random field texture models for classification

Roman Antosik, David R. Scott, Gerald M. Flachs

Research output: Contribution to journalConference article

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. The second approach uses a least squares prediction error model and error signature analysis to model and classify textures. A new statistic and complexity measure are introduced called the K-nearest 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.

Original languageEnglish (US)
Pages (from-to)48-57
Number of pages10
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume1301
StatePublished - 1990
EventDigital Image Processing and Visual Communications Technologies in the Earth and Atmospheric Sciences - Orlando, FL, USA
Duration: Apr 18 1990Apr 19 1990

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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