文档介绍：Multiresolution Wavelet Analysis Based Feature Extraction for Neural
Network Classification
C. H. Chen and G. G. Lee
Electrical puter Engineering Department
University of Massachusetts Dartmouth
285 Old Westport Road
N. Dartmouth, MA 02747
Email: ******@umassd. edu
Abstract
In this paper we introduce a novel feature extraction scheme as a preprocessor for artificial neural
network (ANN) classification. We have shown that the feature extraction scheme implemented via
a non-stationary Gaussian Markov random field (GMRF) based on a multiresolution wavelet
framework can provide effective features for both the ANN and Fuzzy C-Means (FCM)
classification. In our experiment with natural textures and real world digital mammography, each
region of the tested images is assumed to be a different class. A label field with each region or
class being represented by the same grayscale was then found by the back propagation neural
network (BPNN) and FCM clustering algorithm using the extracted discriminatory features.
Further enhancement of the segmented result was achieved via Bayesian learning. The formulation
of this maximum a posteriori (MAP) estimator was based on the Gibbs prior assumption which is
especially appropriate for modeling real world mammograms. Although being estimated by
constrainted optimization, the MAP estimator ca