  Hashing is one of the most popular and powerful approximate nearest neighbor
search techniques for large-scale image retrieval. Most traditional hashing
methods first represent images as off-the-shelf visual features and then
produce hashing codes in a separate stage. However, off-the-shelf visual
features may not be optimally compatible with the hash code learning procedure,
which may result in sub-optimal hash codes. Recently, deep hashing methods have
been proposed to simultaneously learn image features and hash codes using deep
neural networks and have shown superior performance over traditional hashing
methods. Most deep hashing methods are given supervised information in the form
of pairwise labels or triplet labels. The current state-of-the-art deep hashing
method DPSH~\cite{li2015feature}, which is based on pairwise labels, performs
image feature learning and hash code learning simultaneously by maximizing the
likelihood of pairwise similarities. Inspired by DPSH~\cite{li2015feature}, we
propose a triplet label based deep hashing method which aims to maximize the
likelihood of the given triplet labels. Experimental results show that our
method outperforms all the baselines on CIFAR-10 and NUS-WIDE datasets,
including the state-of-the-art method DPSH~\cite{li2015feature} and all the
previous triplet label based deep hashing methods.
