A fast learning algorithm for Gabor transform with applications to image data reduction and pattern classification

A simple neural network-based approach is introduced, which allows the computation of the coefficients of the generalized non-orthogonal 2D 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 based algorithms. The aim is to achieve minimum mean squared error for the reconstructed image from the set of the Gabor coefficients. Applications of this scheme in image data reduction and pattern classification are demonstrated in the simulation results.<
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