A New Model of Computation for Learning Vision Modules from Examples

Abstract

This paper addresses an important class of mimicry problems, where the goal is to construct a computer program which is functionally equivalent to an observed behaviour. Computer vision research can be considered such a challenge, where a researcher attempts to impart human visual abilities to a computer. Unfortunately this has proved a difficult task, not least because our vision processes occur mostly at a subconscious level. It is therefore useful to study the general mimicry problem in order to develop tools which may assist computer vision research. This paper formalises a mimicry problem as one in which a computer learning system (L) constructs a solution from a given program structure (i.e. template or outline) by posing questions to an Oracle. The latter is an entity which, when given an input value, produces the corresponding output of the function which is to be mimicked. In order to define a program‘s structure, particularly one which can be extracted from any computer program automatically, a new model of computation is developed. Based on this a fast algorithm which determines the best questions to pose to the Oracle is then described. Thus L relieves the human programmer of the difficulties faced in choosing the examples from which to learn. This is important because a human programmer might inadvertently choose biased, redundant or otherwise unhelpful examples. Results are shown which demonstrate the utility of a complete learning system (L) based on this work. This paper represents background theory and initial algorithms which further work will extend into powerful automatic learning systems, examples of which are found in [36] and [38].