P. Raveendran et S. Omatu, PERFORMANCE OF AN OPTIMAL SUBSET OF ZERNIKE FEATURES FOR PATTERN-CLASSIFICATION, Information sciences, applications, 1(3), 1994, pp. 133-147
Citations number
17
Categorie Soggetti
Information Science & Library Science","Computer Science Information Systems
This paper presents a technique of selecting an optimal number of feat
ures from the original set of features. Due to the large number of fea
tures considered, it is computationally more efficient to select a sub
set of features that can discriminate as well as the original set. The
subset of features is determined using stepwise discriminant analysis
. The results of using such a scheme to classify scaled, rotated, and
translated binary images and also images that have been perturbed with
random noise are reported. The features used in this study are Zernik
e moments, which are the mapping of the image onto a set of complex or
thogonal polynomials. The performance of using a subset is examined th
rough its comparison to the original set. The classifiers used in this
study are neural network and a statistical nearest neighbor classifie
r. The back-propagation learning algorithm is used in training the neu
ral network. The classifers are trained with some noiseless images and
are tested with the remaining data set. When an optimal subset of fea
tures is used, the classifers performed almost as well as when trained
with the original set of features.