  Hashing aims at generating highly compact similarity preserving code words
which are well suited for large-scale image retrieval tasks.
  Most existing hashing methods first encode the images as a vector of
hand-crafted features followed by a separate binarization step to generate hash
codes. This two-stage process may produce sub-optimal encoding. In this paper,
for the first time, we propose a deep architecture for supervised hashing
through residual learning, termed Deep Residual Hashing (DRH), for an
end-to-end simultaneous representation learning and hash coding. The DRH model
constitutes four key elements: (1) a sub-network with multiple stacked residual
blocks; (2) hashing layer for binarization; (3) supervised retrieval loss
function based on neighbourhood component analysis for similarity preserving
embedding; and (4) hashing related losses and regularisation to control the
quantization error and improve the quality of hash coding. We present results
of extensive experiments on a large public chest x-ray image database with
co-morbidities and discuss the outcome showing substantial improvements over
the latest state-of-the art methods.
