Recognition of rotated images by invariant Karhunen–Loeve expansion

Abstract

Given an ensemble of images, we consider a method for the extraction of optimal features that are invariant under two-dimensional rotations. Our method consists of first identifying some characteristics of the optimal basis functions and then applying the Karhunen–Loeve algorithm to determine them completely. We perform a few classification experiments with the invariant features, using a neural network as a classifier. The new method is compared with the Zernike moments method.