title: Rotation-invariant texture classification using a complete space-frequency model.
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.
authors.affiliation: Ameritech Services, Hoffman Estates, IL 60169, USA. george.m.haley@ameritech.com
authors.forename: G M
authors.initials: GM
authors.lastname: Haley
authors.forename: B S
authors.initials: BS
authors.lastname: Manjunath