Adaptive hyperplane algorithm for texture characterization

Textural features are often consider as one of the most powerful features in describing the intrinsic physical properties of object surfaces in a scene. In this paper, we propose characterizing image textures by a least-squares hyperplane fitting of their image multidimensional primitives. The adaptive implementation of the hyperplane fitting process is carried out by a newly proposed nonlinear supervised neural unit trained by a constrained form of anti-Hebbian learning. Experimental results are presented to demonstrate the performance of the proposed texture characterization model in image classification and segmentation applications