Image classification system based on cortical representations and unsupervised neural network learning

Abstract

A preprocessor based on a computational model of simple cells in the mammalian primary visual cortex is combined with a self-organising artificial neural network classifier. After learning with a sequence of input images, the output units of the system turn out to correspond to classes of input images and this correspondence follows closely human perception. In particular, groups of output units which are selective for images of human faces emerge. In this respect the output units mimic the behaviour of face selective cells that have been found in the inferior temporal cortex of primates. The system is capable of memorising image patterns, building autonomously its own internal representations, and correctly classifying new patterns without using any a priori model of the visual world.