Ricerc@Sapienza

Recognition of objects and regions of interest in digital image processing
often relies on texture classification. The source image is divided according to a
rectangular grid to form textured regions each of which is characterized by some
numerical significant measure called feature. A new approach is introduced that uses
the gray-level spatial dependence matrices and the genetic clustering with unknown
K algorithms to locate sets of homogeneous regions and enhance the discrimination
amongst them. There is no need to select and compute complicated features
transforms as the procedure is based on the optimal weighting of the simple basic
features. A simulation experiment is performed using the well-known Brodatz
textures to demonstrate that the new procedure is able to define well separated clusters
according to the principle of strong internal cohesion and high inter-clusters
separation.