Memorization Learning for Object Recognition

Randal C. Nelson
Department of Computer Science
University of Rochester
Rochester, NY 14627

Abstract: We consider the fundamental complexity of intelligent systems, and argue that some form of learning is essential in order to acquire the amount of information necessary to specify such a system. In this view, learning is essentially a means of acquiring large amounts of structured information by relatively simple recoding of information flowing down high-bandwidth channels (the senses) from a pre-existing source of structured information (the world). This chapter considers the case of visual learning. We argue that, of the known methods of computational learning, the only technique that can effectively assemble the very large amount of information needed to specify visual intelligence amounts to the construction of an indexed memory. We show how an associative memory organization coupled with the use of robust, semi-invariant features can lead to reliable 3-D object recognition.