  The advent of a panoply of resource limited devices opens up new challenges
in the design of computer vision algorithms with a clear compromise between
accuracy and computational requirements. In this paper we present new binary
image descriptors that emerge from the application of triplet ranking loss,
hard negative mining and anchor swapping to traditional features based on pixel
differences and image gradients. These descriptors, BAD (Box Average
Difference) and HashSIFT, establish new operating points in the
state-of-the-art's accuracy vs.\ resources trade-off curve. In our experiments
we evaluate the accuracy, execution time and energy consumption of the proposed
descriptors. We show that BAD bears the fastest descriptor implementation in
the literature while HashSIFT approaches in accuracy that of the top deep
learning-based descriptors, being computationally more efficient. We have made
the source code public.
