Unsupervised classification of texture images by gray-level spatial dependence matrices and genetic algorithms
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