Relational Reinforcement Learning Applied to Appearance-Based Object Recognition

In this paper we propose an adaptive, self-learning system, which utilizes relational reinforcement learning (RRL), and apply it to a computer vision problem. A common problem in computer vision consists in the discrimination between similar objects which differ in salient features visible from distinct views only. Usually existing object recognition systems have to scan an object from a large number of views for a reliable discrimination. Optimization is achieved at most with heuristics to reduce the amount of computing time or to save storage space. We apply RRL in an appearance-based approach to the problem of discriminating similar objects, which are presented from arbitray views. We are able to rapidly learn scan paths for the objects and to reliably distinguish them from only a few recorded views. The appearance-based approach and the possibility to define states and actions of the RRL system with logical descriptions allow for a large reduction of the dimensionality of the state space and thus save storage and computing time.