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

We present a new method of fractal-based texture analysis, using the multiscale fractional Brownian motiontexture model, and a new parameter, intermittency. The intermittency parameter describes a degree of presence of the textural information: a low value of implies a very lacunar texture. The multi-scale fractional Brownian motion model allows to construct multiregime 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 and depending on a scale . The texture is thereby seen as the fusion of structures and details. The structure of the texture is analyzed with the large values of , corresponding to the low frequency content of the texture. The details of the texture are analyzed with the small values of , related to the high frequency content of the texture. The texture is then characterized by all the estimated values of and , for all the scales of analysis. The method allows a multi-frequency analysis, permitting the choice of significant scales in a classification task. An application to the classification of corn silage texture images, for which the low frequency content is determining, is proposed.