Unsupervised extraction of structural information from high dimensional visual data

We present three unsupervised artificial neural networks for the extraction of structural information from visual data. The ability of each network to represent structured knowledge in a manner easily accessible to human interpretation is illustrated using artificial visual data. These networks are used to collectively demonstrate a variety of unsupervised methods for identifying features in visual data and the structural representation of these features in terms of orientation, temporal and topographical ordering, and stereo disparity.