Generic object recognition with local features: From bags to subspaces

We propose an alternative approach to the widely-used Bag-of-Features (BoF) for representing objects in terms of a collection of local features extracted from their images. In this new framework, called Subspaces-of-Features (SoF), first the sets of local features extracted from the images of the objects are represented as low-dimensional linear subspaces. Then the subspaces corresponding to different categories are orthogonalized, and the similarity between subspaces corresponding to different categories is calculated using the Grassmannian distances defined through the principal angles between the subspaces. The performance of SoF is illustrated on a standard generic object recognition benchmark.