Shared Features for Scalable Appearance-Based Object Recognition

We present a framework for learning object representations for fast recognition of a large number of different objects. Rather than learning and storing feature representations separately for each object, we create a finite set of representative features and share these features within and between different object models. In contrast to traditional recognition methods that scale linearly with the number of objects, the shared features can be exploited by bottom-up search algorithms which require a constant number of feature comparisons for any number of objects. We demonstrate the feasibility of this approach on a novel database of 50 everyday objects in cluttered real-world scenes. Using Gabor wavelet-response features extracted only at corner points, our system achieves good recognition results despite substantial occlusion and background clutter.