Title :
A fast learning algorithm for Gabor transform extraction
Author :
Ibrahim, Ayman E. ; Sadjadi, Mahmood R Azimi ; Sheedvash, Sassan
Author_Institution :
Dept. of Electr. Eng., Colorado State Univ., Fort Collins, CO, USA
fDate :
30 May-2 Jun 1994
Abstract :
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
Keywords :
convergence of numerical methods; data compression; image coding; image recognition; image representation; image segmentation; learning (artificial intelligence); least squares approximations; neural nets; transforms; Gabor coefficients; Gabor transform extraction; RLS learning algorithm; convergence; fast learning algorithm; image data reduction; minimum mean squared error; neural network-based approach; nonorthogonal 2D Gabor transform representation; reconstructed image; recursive least squares; Convergence; Data compression; Discrete transforms; Fourier transforms; Image reconstruction; Image representation; Lattices; Least squares approximation; Neural networks; Time frequency analysis;
Conference_Titel :
Circuits and Systems, 1994. ISCAS '94., 1994 IEEE International Symposium on
Conference_Location :
London
Print_ISBN :
0-7803-1915-X
DOI :
10.1109/ISCAS.1994.409163
