The effects of training algorithms in MLP network on image classification

Abstract

This paper presents the use of Multilayer Perceptrons (MLP) trained with various training algorithms for image analysis and pattern recognition. Given a data set of images with known classifications, a system can predict the classification of new images. However, the accuracy of the networks, having the same size and same learning parameters, changes according to training algorithm used in MLP. The effects of the different algorithms are investigated and the best learning methods were proposed for image segmentation.