Texture classification through multiscale orientation histogram analysis

Abstract

This work presents a new approach to texture classification, in which orientation histograms and multiscale analysis have been combined to achieve a reliable method. From the outputs of a set of filters, the orientation and magnitude of the gradient in every point of a texture are estimated. By combining the orientations and relative magnitudes of the gradient, we build an orientation histogram for each texture. We have used Fourier analysis to measure the similarity between the histograms of different textures, considering the effects of a change in the size or orientation of the image to make our method invariant under these phenomena. Since different textures may generate very similar histograms, we have analyzed the evolution of these histograms at different scales, extracting a scale factor for each couple of compared textures to adjust the filters which are applied to them when the multiscale analysis is carried out.