  Mobile visual search applications are emerging that enable users to sense
their surroundings with smart phones. However, because of the particular
challenges of mobile visual search, achieving a high recognition bitrate has
becomes a consistent target of previous related works. In this paper, we
propose a few-parameter, low-latency, and high-accuracy deep hashing approach
for constructing binary hash codes for mobile visual search. First, we exploit
the architecture of the MobileNet model, which significantly decreases the
latency of deep feature extraction by reducing the number of model parameters
while maintaining accuracy. Second, we add a hash-like layer into MobileNet to
train the model on labeled mobile visual data. Evaluations show that the
proposed system can exceed state-of-the-art accuracy performance in terms of
the MAP. More importantly, the memory consumption is much less than that of
other deep learning models. The proposed method requires only $13$ MB of memory
for the neural network and achieves a MAP of $97.80\%$ on the mobile location
recognition dataset used for testing.
