Probabilistic Neural Network for pattern classification

In this paper, a Neural Network (NN) based approach for classification of images which are invariant to translation, scale and rotation is presented. The utilized network is a Probabilistic Neural Network (PNN) classifier. The translation and scale invariant features are obtained by Fourier-Modified Direct Mellin Transform (F-MDMT) method. The rotation invariancy is obtained by Zernike moments. The classification accuracy was compared to Bayes classifier, K-nearest neighborhood, minimum mean distance and MLP classifier. But the PNN classifier gives better accuracy when compared with other methods. Similarly, F-MDMT combined with Zernike moment method was found to be better when compared with other methods like geometrical method, etc. The input data was all 49 different characters of Oriya language having each pixel size 64 X 64. The noise analysis was carried out and is found that the present technique adopted in this paper is better so far as input noise is considered. This method can also be suitable for any type of 3-D object recognition.