  Unsupervised feature learning has shown impressive results for a wide range
of input modalities, in particular for object classification tasks in computer
vision. Using a large amount of unlabeled data, unsupervised feature learning
methods are utilized to construct high-level representations that are
discriminative enough for subsequently trained supervised classification
algorithms. However, it has never been \emph{quantitatively} investigated yet
how well unsupervised learning methods can find \emph{low-level
representations} for image patches without any additional supervision. In this
paper we examine the performance of pure unsupervised methods on a low-level
correspondence task, a problem that is central to many Computer Vision
applications. We find that a special type of Restricted Boltzmann Machines
(RBMs) performs comparably to hand-crafted descriptors. Additionally, a simple
binarization scheme produces compact representations that perform better than
several state-of-the-art descriptors.
