Image recognition via Bayesian likelihood analysis of wavelet coefficients

Authors:
Ester Yen;I-Wen Mike Chu
Affiliations:
Department of Mathematics, Imperial College, London, UK;NASA Goddard Flight Space Center, Greenbelt, MD, USA
Venue:
Expert Systems with Applications: An International Journal
Year:
2008

Citing 7
Cited 0

Optical character recognition by the method of moments

Computer Vision, Graphics, and Image Processing
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Handwritten digit recognition with a back-propagation network

Advances in neural information processing systems 2
A training algorithm for optimal margin classifiers

COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Transform Coding of Images

Transform Coding of Images
Efficient Pattern Recognition Using a New Transformation Distance

Advances in Neural Information Processing Systems 5, [NIPS Conference]
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images

IEEE Transactions on Pattern Analysis and Machine Intelligence

Quantified Score

Hi-index	12.05

Visualization

Abstract

This study experiments with a Bayesian approach in recognition of images utilizing a joint-form likelihood of wavelet coefficients built from decomposition. Images of handwritten numerals are attacked via the Mallet decomposition algorithm with Daubechies wavelets to extract the feature vectors of coefficients. The model assumes the coefficient vectors by multivariate normal distributions and employs a Bayesian approach for classification based on the joint form of distributions. The results demonstrate marked improvement in recognition performance at the second level of decomposition.