Learning paradigms for image interpretation

In this paper we discuss image understanding and object recognition as a class of processes which involve binding what is seen with what is known. It follows from this perspective that it is important to explicate how systems may learn about spatial information from images, how it is encoded, and, how, in turn, such knowledge is matched with new image data. As well as discussing very specific solutions to these problems, this paper questions the view that our scientific understanding of image understanding is sufficiently circumscribed by the study of how image features are extracted and matched, per se.