Visual learning of patterns and objects

We discuss automatic rule generation techniques for learning relational properties of 2D visual patterns and 3D objects from training samples where the observed feature values are continuous. In particular, we explore a conditional rule generation method that defines patterns (or objects) in terms of ordered lists of bounds on unary (pattern part) and binary (part relation) features. The technique, termed conditional rule generation, was developed to integrate relational structure representations of patterns and the generalization characteristics of evidenced-based systems. We show how this technique can be used for recognition of complex patterns and of objects in scenes. Further, we show the extent to which the learned rules can identify patterns and objects that have undergone nonrigid distortions.