Rotation-invariant texture classification using a complete space-frequency model

  • Authors:
  • G. M. Haley;B. S. Manjunath

  • Affiliations:
  • Ameritech Services, Hoffman Estates, IL;-

  • Venue:
  • IEEE Transactions on Image Processing
  • Year:
  • 1999

Quantified Score

Hi-index 0.01

Visualization

Abstract

A method of rotation-invariant texture classification based on a complete space-frequency model is introduced. A polar, analytic form of a two-dimensional (2-D) Gabor wavelet is developed, and a multiresolution family of these wavelets is used to compute information-conserving microfeatures. From these microfeatures a micromodel, which characterizes spatially localized amplitude, frequency, and directional behavior of the texture, is formed. The essential characteristics of a texture sample, its macrofeatures, are derived from the estimated selected parameters of the micromodel. Classification of texture samples is based on the macromodel derived from a rotation invariant subset of macrofeatures. In experiments, comparatively high correct classification rates were obtained using large sample sets