A new texture representation with multi-scale wavelet feature

The existing methods for texture modeling include co-occurrence statistics, filter banks and random fields. However most of these methods lack of capability to characterize the different scale of texture effectively. In this paper, we propose a texture representation which combines local scale feature, amplitude and phase of wavelet modules in multi-scales. The self-similarity of texture is not globally uniform and could be measured in both correlations across the multi-scale and statistical feature within a single-scale. In our approach, the local scale feature is represented by optimal scale obtained through the evolution of wavelet modulus across multi-scales. Then, for all the blocks of the same optimal scale, the statistical measurement of amplitude is extracted to represent the energy within the corresponding frequency band; the statistical measurement of the phase of modulus is extracted to represent the texture's orientation. Our experiment indicates that, in the proposed texture representation the separability of different texture patterns is larger than the one of the traditional features.