Rotation-invariant texture classification using a complete space-frequencymodel

A method of rotation-invariant texture classification based on a complete s pace-frequency model is introduced. A polar, analytic form of a two-dimensi onal (2-D) Gabor wavelet is developed, and a multiresolution family of thes e wavelets is used to compute information-conserving microfeatures. From th ese microfeatures a micromodel, which characterizes spatially localized amp litude, frequency, and directional behavior of the texture, is formed. The essential characteristics of a texture sample, its macrofeatures, are deriv ed from the estimated selected parameters of the micromodel, Classification of texture samples is based on the macromodel derived from a rotation inva riant subset of macrofeatures, In experiments, comparatively high correct c lassification rates were obtained using large sample sets.