We present an adaptive method that allows the classification of the regions of an image in two classes: textured and uniform regions (weak gray-level variance). Information on the type of texture (stochastic, deterministic) and its granularity (macroscopic, microscopic) are extracted. The developed method is achieved in two steps. It first enables the determination of the global context of an image (image mainly composed of uniform areas or textured ones) and the localization of the textured and uniform areas. The second step characterizes each detected area by considering some appropriate features: mean and variance in the case of uniform regions and classical relevant texture attributes (derived from a statistical analysis) associated with some new attributes that we define in the case of textured regions. These complementary features are determined from a texture model derived from the Wold decomposition of the autocovariance function. They enable the acquisition of some information on the type of texture and its granularity. We show the efficiency of the method through two examples of image segmentation.