Generalization to Novel Views: Universal, Class-based, and Model-based Processing

摘要

A major problem in object recognition is that a novel image of a given object can be different from all previously seen images. Images can vary considerably due to changes in viewing conditions such as viewing position and illumination. In this paper we distinguish between three types of recognition schemes by the level at which generalization to novel images takes place: universal, class, and model-based. The first is applicable equally to all objects, the second to a class of objects, and the third uses known properties of individual objects. We derive theoretical limitations on each of the three generalization levels. For the universal level, previous results have shown that no invariance can be obtained. Here we show that this limitation holds even when the assumptions made on the objects and the recognition functions are relaxed. We also extend the results to changes of illumination direction. For the class level, previous studies presented specific examples of classes of objects for which functions invariant to viewpoint exist. Here, we distinguish between classes that admit such invariance and classes that do not. We demonstrate that there is a tradeoff between the set of objects that can be discriminated by a given recognition function and the set of images from which the recognition function can recognize these objects. Furthermore, we demonstrate that although functions that are invariant to illumination direction do not exist at the universal level, when the objects are restricted to belong to a given class, an invariant function to illumination direction can be defined. A general conclusion of this study is that class-based processing, that has not been used extensively in the past, is often advantageous for dealing with variations due to viewpoint and illuminant changes.