Combined statistical-fractal wavelets signatures for texture recognition

When characterizing textures in the scope of recognition or segmentation, one can choose from a great number of existing features. Among them, features based on the wavelet decomposition provide good results and are already used in many applications. One key point for the success of these methods is the choice of the signature used to describe the sub-bands. The energy signature is the most popular, but others exist, with better efficiency. In this paper, we review some of them and bring improvements in their computation. We also show that combining spatial and statistical signatures increase their performance in texture classification problematics.