  Traditional person re-identification (ReID) methods typically represent
person images as real-valued features, which makes ReID inefficient when the
gallery set is extremely large. Recently, some hashing methods have been
proposed to make ReID more efficient. However, these hashing methods will
deteriorate the accuracy in general, and the efficiency of them is still not
high enough. In this paper, we propose a novel hashing method, called deep
multi-index hashing (DMIH), to improve both efficiency and accuracy for ReID.
DMIH seamlessly integrates multi-index hashing and multi-branch based networks
into the same framework. Furthermore, a novel block-wise multi-index hashing
table construction approach and a search-aware multi-index (SAMI) loss are
proposed in DMIH to improve the search efficiency. Experiments on three widely
used datasets show that DMIH can outperform other state-of-the-art baselines,
including both hashing methods and real-valued methods, in terms of both
efficiency and accuracy.
