CLASSIFICATION OF INVARIANT IMAGE REPRESENTATIONS USING A NEURAL NETWORK

In this paper, a neural network (NN) based approach for classification of images represented by translation-, scale-, and rotation-invariant features is presented. The utilized network is a multilayer perceptron (MLP) classifier with one hidden layer. The back-propagation learning is used for its training. Two types of features are used: moment invariants derived from geometrical moments of the image, and the newly developed Zernike moment based features. Zernike moments are the mapping of the image onto a set of complex orthogonal polynomials. The performance of the MLP is compared to those of three other traditional statistical classifiers, namely, Bayes, nearest-neighbor, and minimum-mean-distance. Through extensive experimentation with noiseless as well as noisy binary images of all English characters (26 classes), the following conclusions are reached: 1) the MLP outperforms the other three classifiers, especially when noise is present, 2) the nearest-neighbor classifier performs about the same as the NN for the noiseless case, 3) the NN can do well even with a very small number of training samples, 4) the NN has a good degree of fault tolerance, and 5) the Zernike moment based features possess strong class separability power and are more powerful than moment invariants. © 1990 IEEE