  We present a powerful new loss function and training scheme for learning
binary hash functions. In particular, we demonstrate our method by creating for
the first time a neural network that outperforms state-of-the-art Haar wavelets
and color layout descriptors at the task of automated scene matching. By
accurately relating distance on the manifold of network outputs to distance in
Hamming space, we achieve a 100-fold reduction in nontrivial false positive
rate and significantly higher true positive rate. We expect our insights to
provide large wins for hashing models applied to other information retrieval
hashing tasks as well.
