  Some mixtures, such as colloids like milk, blood, and gelatin, have
homogeneous appearance when viewed with the naked eye, however, to observe them
at the nanoscale is possible to understand the heterogeneity of its components.
The same phenomenon can occur in pattern recognition in which it is possible to
see heterogeneous patterns in texture images. However, current methods of
texture analysis can not adequately describe such heterogeneous patterns.
Common methods used by researchers analyse the image information in a global
way, taking all its features in an integrated manner. Furthermore, multi-scale
analysis verifies the patterns at different scales, but still preserving the
homogeneous analysis. On the other hand various methods use textons to
represent the texture, breaking texture down into its smallest unit. To tackle
this problem, we propose a method to identify texture patterns not small as
textons at distinct scales enhancing the separability among different types of
texture. We find sub patterns of texture according to the scale and then group
similar patterns for a more refined analysis. Tests were performed in four
static texture databases and one dynamic one. Results show that our method
provides better classification rate compared with conventional approaches both
in static and in dynamic texture.
