Classification of rotated and scaled textures using HMHV spectrum estimation and the Fourier-Mellin transform

Abstract

In the framework of image modeling for texture analysis, we propose the combination of the new parametric 2-D spectrum estimation method called HMHV (harmonic mean horizontal vertical) and the Fourier-Mellin transform. This latter technique allows the calculation of a set of texture descriptors from a 2-D spectrum estimate which is invariant under rotation and scaling. A comparison of the HMHV and the standard parametric HM (harmonic mean) methods on synthetic and natural stochastic textures shows that the HMHV method presents almost no spurious peaks and is quite isotropic. By performing the classification of a set of 60 images divided in 12 texture classes, descriptors computed with the HMHV method provide better results than those computed with the HM method.