Generic object class detection using boosted configurations of oriented edges

Abstract

In this paper we introduce a new representation for shape-based object class detection. This representation is based on very sparse and slightly flexible configurations of oriented edges. An ensemble of such configurations is learnt in a boosting framework. Each edge configuration can capture some local or global shape property of the target class and the representation is thus not limited to representing and detecting visual classes that have distinctive local structures. The representation is also able to handle significant intra-class variation. The representation allows for very efficient detection and can be learnt automatically from weakly labelled training images of the target class. The main drawback of the method is that, since its inductive bias is rather weak, it needs a comparatively large training set. We evaluate on a standard database [1] and when using a slightly extended training set, our method outperforms state of the art [2] on four out of five classes. © 2011 Springer-Verlag Berlin Heidelberg.