Finding similar images in a large database is an important, but often
computationally expensive, task. In this paper, we present a two-tier similar-image
retrieval system with the efficiency characteristics found in simpler systems
designed to recognize near-duplicates. We compare the efficiency of lookups based
on random projections and learned hashes to 100-times-more-frequent exemplar
sampling. Both approaches significantly improve on the results from exemplar
sampling, despite having significantly lower computational costs. Learned-hash keys
provide the best result, in terms of both recall and efficiency.