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.
Our article presents a new way to characterize texture : Wavelet Geometrical Features, that extracts structural measurements from wavelet sub-bands, when most of the wavelet-based methods found in the litterature use only statistical ones. We first describe the method used to compute our features, and thereafter compare them
to thirteen other standard texture features in a classification experiment on the whole Brodatz texture database. We showed that our method produces the best results, especially over the wavelet energy signature and the method it originated from, the Statistical Geometrical Features of Chen.