Classification of Texture Images using Multi-scale StatisticalEstimators of Fractal Parameters

We present a new method of fractal-based texture analysis, using the multi-scale fractional Brownian motion texture model, and a new parameter, in-termittency. The intermittency parameter p describes a degree of presence of the textural information: a low value of p implies a very lacunar texture. The multi-scale fractional Brownian motion model allows to construct multi-regime textures in the frequency domain. Adding intermittency to this model, we compose the intermittent multi-scale fractional Brownian motion model: the Hurst and intermittency parameters of such processes are functions H(l) and p(l) depending on a scale l. The texture is thereby seen as the fusion of structures and details. The structure of the texture is analyzed with the large values of l, corresponding to the low frequency content of the texture. The details of the texture are analyzed with the small values of l, related to the high frequency content of the texture. The texture is then characterized by all the estimated values of H(l) and p(l), for all the scales l of analysis. The method allows a multi-frequency analysis, permitting the choice of sig-nificant scales in a classification task. An application to the classification of corn silage texture images, for which the low frequency content is determin-ing, is proposed. Keywords. Texture, fractal, multi-scale and multi-frequency analysis, multi-scale fractional Brownian motion, intermit-tency, Hurst parameter, statistical estimators, self-similarity, image classification. 1