Acquisition of visual shape primitives.

Abstract

Shape primitives have long been proposed as components for object models in the visual system, and account for a considerable body of behavioral findings. While a large amount of effort has been devoted to the study of detection of these parts in the scenes, no research has been undertaken simulating the acquisition of these representations. We present a model which suggests how the shape primitives may be learned by experience in a self-organized fashion. This model offers the first successful unsupervised learning of shape primitives which are as complex as object parts and can serve as intermediate representations for various objects. The algorithm uses synthetic gray-level objects, each composed of several parts (primitives or else), and shape primitives emerge as a result of partial matches between several objects. Our algorithm does not use any a priori knowledge about any attributes of the patterns to be learned; and the recurrence of these visual patterns in various objects is the only basis for their emergence as new features.