Learning location invariance for object recognition and localization

Abstract

A visual system not only needs to recognize a stimulus, it also needs to find the location of the stimulus. In this paper, we present a neural network model that is able to generalize its ability to identify objects to new locations in its visual field. The model consists of a feedforward network for object identification and a feedback network for object location. The feedforward network first learns to identify simple features at all locations and therefore becomes selective for location invariant features. This network subsequently learns to identify objects partly by learning new conjunctions of these location invariant features. Once the feedforward network is able to identify an object at a new location, all conditions for supervised learning of additional, location dependent features for the object are set. The learning in the feedforward network can be transferred to the feedback network, which is needed to localize an object at a new location. © Springer-Verlag Berlin Heidelberg 2005.