Application of Wavelets and Mahalanobis Distances to Robust Design of an Image Classification System

Abstract

A system for classifying images in the presence of noise was developed and optimized using Robust Design methods. The image recognition system uses wavelet transforms to extract features from images. These features were used to construct Mahalanobis spaces for each type of image in a set. The system classifies noisy images by comparing the Mahalanobis distance to all of the image types in the set and selecting the image type with the smallest distance. The system was tested using gray-scale bitmaps of four famous portraits. Robust Design methods were employed to optimize the selection of image features used to construct the Mahalanobis space. The optimized system employs only 14 coefficients for classification and correctly classifies more than 99% of the noisy images presented to it.