  This work focuses on representing very high-dimensional global image
descriptors using very compact 64-1024 bit binary hashes for instance
retrieval. We propose DeepHash: a hashing scheme based on deep networks. Key to
making DeepHash work at extremely low bitrates are three important
considerations -- regularization, depth and fine-tuning -- each requiring
solutions specific to the hashing problem. In-depth evaluation shows that our
scheme consistently outperforms state-of-the-art methods across all data sets
for both Fisher Vectors and Deep Convolutional Neural Network features, by up
to 20 percent over other schemes. The retrieval performance with 256-bit hashes
is close to that of the uncompressed floating point features -- a remarkable
512 times compression.
