Eigenboosting: Combining Discriminative and Generative Information
Horst Bischof (TU Graz, Austria)
A major shortcoming of discriminative recognition and detection methods
is their noise sensitivity, both during training and recognition. This
may lead to very sensitive and brittle recognition systems focusing on
irrelevant information. This paper proposes to augment a discriminative
classifier by reconstructive (generative) information. In particular, we
boost classical Haar-like features and use the same features to
approximate a generative model
(\ie, eigenimages). A modified error function for boosting ensures that
only features are selected that show a good discrimination and
reconstruction. This allows a robust feature selection using boosting.
Thus, we can handle problems where discriminant classifiers usually fail
while still retaining the discriminative power. Our experiments show that
we can significantly improve the recognition performance when learning
from noisy data. Moreover, the used feature type allows efficient
recognition and reconstruction.