View-Invariant Recognition Using Corresponding Object Fragments

Evgeniy Bart, Evgeny Byvatov, Shimon Ullman

Abstract: We develop a novel approach to view-invariant recognition and apply it to the task of recognizing face images under widely separated viewing directions. Our main contribution is a novel object representation scheme using ‘extended fragments’ that enables us to achieve a high level of recognition performance and generalization across a wide range of viewing conditions. Extended fragments are equivalence classes of image fragments that represent informative object parts under different viewing conditions. They are extracted automatically from short video sequences during learning. Using this representation, the scheme is unique in its ability to generalize from a single view of a novel object and compensate for a significant change in viewing direction without using 3D information. As a result, novel objects can be recognized from viewing directions from which they were not seen in the past. Experiments demonstrate that the scheme achieves significantly better generalization and recognition performance than previously used methods.