Computational Theories of Object Recognition

Abstract

Visual categorization, or making sense of novel shapes and shape classes, is a computationally challenging and behaviorally important task, which is not widely addressed in computer vision or visual psychophysics (where the stress is rather on the generalization of recognition across changes of viewpoint). This paper examines the categorization abilities of four current approaches to object representation: structural descriptions, geometric models, multidimensional feature spaces, and similarities to reference shapes. It is proposed that a scheme combining features of all four approaches is a promising candidate for a comprehensive and computationally feasible theory of categorization