Visual Recognition and Categorization on the Basis of Similarities to Multiple Class Prototypes

Abstract

One of the difficulties of object recognition stems from the need to overcome the variability in object appearance caused by factors such as illumination and pose. The influence of these factors can be countered by learning to interpolate between stored views of the target object, taken under representative combinations of viewing conditions. Difficulties of another kind arise in daily life situations that require categorization, rather than recognition, of objects. We show that, although categorization cannot rely on interpolation between stored examples, knowledge of several representative members, or prototypes, of each of the categories of interest can still provide the necessary computational substrate for the categorization of new instances. The resulting representational scheme based on similarities to prototypes is computationally viable, and is readily mapped onto the mechanisms of biological vision revealed by recent psychophysical and physiological studies. 1 Introduction T..