Self-organizing segmentor and feature extractor

Proposes a novel approach to segmentation using a combination of Hebbian learning and competitive learning in a self-organizing manner. The network is modular, with each module corresponding to a different class of the input data. A module consists of a weight vector that is calculated during an initial training period. The appropriate class for a given input vector is determined by a maximum entropy classifier. The resulting network consistently extracts perceptually relevant features from image data. As well, the class representations are analogous to the arrangement of directionally sensitive columns in the visual cortex.<>