FRACTAL-BASED CLASSIFICATION OF NATURAL TEXTURES

Texture classification is an important first step In image segmentatio n and image recognition, The classification algorithm must be able to overcome distortions, such as scale, aspect, and rotation changes in t he input texture. In this paper, a new fractal model for texture class ification is presented, The model is based on fractional Brownian moti on (FBM). It is also shown that this model is invariant to changes in incident light; empirical results are also given, The isotropic nature of Brownian motion is particularly useful for outdoor applications, w here the viewing direction may change. Classification results of this model are presented; comparisons with other texture measurement models indicate that the incremental FBM (IFBM) model has better performance for the samples tested.