ABSTRACT
An important function of human visual perception is to permit object classification at multiple levels of specificity. For example, we can recognize an object as a "car," (the basic level) a "Ford Mustang" (subordinate level), and "Joe's Mustang" (instance level). Although this capacity is fundamental to human object perception, most computational models of object recognition either focus exclusively on basic-level classification (e.g., Biederman, 1987; Hummel & Biederman, 1992; Hummel & Stankiewicz, 1996) or exclusively on instance-level classification (e.g., Ullman & Basri, 1991; Edelman & Poggio, 1990). A computational account that naturally integrates both levels of classification remains elusive. We describe a general approach to representing numerical properties (e.g., those that characterize object shape) that simultaneously supports both basic and subordinate/instance-level recognition. The account is based on a general nonlinear coding for numerical quantities describing both featural variables (such as degree of curvature and aspect ratio) and configural variables (such as relative position). Used as the input to a classifier with Gaussian receptive fields, this representation supports recognition at multiple levels of specificity, and suggests an account of the role of attention and time in the classification of objects at different levels of abstraction.
