N. B. Ampilova, V. D. Sergeev, I. P. Soloviev
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Now it is widely accepted that many digital images are phase portraits of complex dynamical systems. The distribution of system trajectories in the phase space may be described by a measure that follows an exponential law. In this work, we consider methods of obtaining classifi cation signs based on the calculation of alpha-divergences (Rényi divergences), the Hausdorff dimension of measure support, and averaged singularity exponents. A discrete normed measure and the sequence of measures obtained from the initial one by the direct multifractal transform are considered for any given image. In the fi rst method, we compare two images by calculating the alpha-divergence between the measures from corresponding sequences. The obtained vector
is a characteristic of similarity of images structures. In the second method, we calculate the Hausdorff dimension of the measure support and the averaged singularity exponent. The results of numerical experiments for Brodatz textures and biomedical preparation images are given.
Keywords: image analysis, probabilistic measure, multifractal spectrum, Rényi divergences, direct multifractal transform, Hausdorff dimension.
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