  Image hashing is a popular technique applied to large scale content-based
visual retrieval due to its compact and efficient binary codes. Our work
proposes a new end-to-end deep network architecture for supervised hashing
which directly learns binary codes from input images and maintains good
properties over binary codes such as similarity preservation, independence, and
balancing. Furthermore, we also propose a new learning scheme that can cope
with the binary constrained loss function. The proposed algorithm not only is
scalable for learning over large-scale datasets but also outperforms
state-of-the-art supervised hashing methods, which are illustrated throughout
extensive experiments from various image retrieval benchmarks.
