Classifying Objects from Visual Information
Consider a world of 'objects.' Our goal is to place these objects into categories that are useful to the observer using sensory data. One criterion for utility is that the categories allow the observer to infer the object's potential behaviors, which are often non-observable. Under what condidtions can such useful categories be created? We propose a solution which requires 1.) that modes or clusters of natural structures are present in the world, and, 2.) that the physical properties of these structures are reflected in the sensory data used by the observer for classification. Given these two constraints, we explore the type of additional knowledge sufficient for the observer to generate an internal representation that makes explicit the natural modes. Finally we develop a formal expression of the object classification problem.