  Here we propose a new method for the classification of texture images
combining fractal measures (fractal dimension, multifractal spectrum and
lacunarity) with local binary patterns. More specifically we compute the box
counting dimension of the local binary codes thresholded at different levels to
compose the feature vector. The proposal is assessed in the classification of
three benchmark databases: KTHTIPS-2b, UMD and UIUC as well as in a real-world
problem, namely the identification of Brazilian plant species (database
1200Tex) using scanned images of their leaves. The proposed method demonstrated
to be competitive with other state-of-the-art solutions reported in the
literature. Such results confirmed the potential of combining a powerful local
coding description with the multiscale information captured by the fractal
dimension for texture classification.
