  We present a novel means of describing local image appearances using binary
strings. Binary descriptors have drawn increasing interest in recent years due
to their speed and low memory footprint. A known shortcoming of these
representations is their inferior performance compared to larger, histogram
based descriptors such as the SIFT. Our goal is to close this performance gap
while maintaining the benefits attributed to binary representations. To this
end we propose the Learned Arrangements of Three Patch Codes descriptors, or
LATCH. Our key observation is that existing binary descriptors are at an
increased risk from noise and local appearance variations. This, as they
compare the values of pixel pairs; changes to either of the pixels can easily
lead to changes in descriptor values, hence damaging its performance. In order
to provide more robustness, we instead propose a novel means of comparing pixel
patches. This ostensibly small change, requires a substantial redesign of the
descriptors themselves and how they are produced. Our resulting LATCH
representation is rigorously compared to state-of-the-art binary descriptors
and shown to provide far better performance for similar computation and space
requirements.
