Title :
A fast learning algorithm for Gabor transform with applications to image data reduction and pattern classification
Author :
Ibrahim, Ayman E. ; Sadjadi, Mahmood R Azimi ; Sheedvash, Sassan
Author_Institution :
Dept. of Electr. Eng., Colorado State Univ., Fort Collins, CO, USA
fDate :
27 Jun-2 Jul 1994
Abstract :
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
Keywords :
data reduction; image classification; image reconstruction; learning (artificial intelligence); least squares approximations; neural nets; transforms; Gabor coefficients; Gabor transform; convergence; fast learning algorithm; image data reduction; image reconstruction; minimum mean squared error; neural network; pattern classification; recursive least squares learning; Convergence; Data compression; Fourier transforms; Frequency; Image reconstruction; Least squares approximation; Least squares methods; Neural networks; Pattern classification; Resonance light scattering;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374962
