Multifractal texture analysis using a dilation-based hölder exponent

We present an approach to extract descriptors for the analysis of grey-level textures in images. Similarly to the classical multifractal analysis, the method subdivides the texture into regions according to a local Hölder exponent and computes the fractal dimension of each subset. However, instead of estimating such exponents (by means of the mass-radius relation, wavelet leaders, etc.) we propose using a local version of Bouligand- Minkowski dimension. At each pixel in the image, this approach provides a scaling relation which fits better to what is expected from a multifractal model than the direct use of the density function. The performance of the classification power of the descriptors obtained with this method was tested on the Brodatz image database and compared to other previously published methods used for texture classification. Our method outperforms other approaches confirming its potential for texture analysis.