A fast learning algorithm for Gabor transform extraction

A simple neural network-based approach is introduced in this paper which allows the computation of the coefficients of the generalized non-orthogonal 2-D Gabor transform representation. The network is trained using a recursive least squares (RLS) type algorithm. This RLS learning algorithm offers better accuracy and faster convergence when compared to the least mean squares (LMS) based algorithms. The aim is to achieve minimum mean squared error for the reconstructed image from the set of the Gabor coefficients. Application of this scheme in image data reduction is demonstrated in the simulation results.<>