Limitations of Non Model-Based Recognition Schemes

Abstract
Different approaches to visual object recognition can be divided into two general classes: model-based vs. non model-based schemes. In this paper we establish some limitation on the class of non model-based recognition schemes. a non model-based scheme is based on functions invariant to viewing position and illumination conditions. We show that every function that is invariant to viewing position of all objects is the trivial (constant) function. The same result holds even if the recognition function is not required to be perfect, but is allowed to make mistakes and misidentify each object from a substantial fraction of viewing directions. It follows that every consistent recognition scheme for recognizing Three dimensional objects must in general be model based. We then consider recognition schemes restricted to classes of objects and show that, for some classes, the only consistent recognition function is still the trivial function. For other classes (such as the class of symmetric objects) a nontrivial recognition scheme exists. We define the notion of a discrimination power of a consistent recognition function for a class of objects. The function's discrimination power determines the set of objects that can be discriminated by the recognition function. We show that it is possible to determine the upper bound of the function's discrimination power for every consistent recognition function.

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