  Deep hashing methods have received much attention recently, which achieve
promising results by taking advantage of the strong representation power of
deep networks. However, most existing deep hashing methods learn a whole set of
hashing functions independently, while ignore the correlations between
different hashing functions that can promote the retrieval accuracy greatly.
Inspired by the sequential decision ability of deep reinforcement learning, we
propose a new Deep Reinforcement Learning approach for Image Hashing (DRLIH).
Our proposed DRLIH approach models the hashing learning problem as a sequential
decision process, which learns each hashing function by correcting the errors
imposed by previous ones and promotes retrieval accuracy. To the best of our
knowledge, this is the first work to address hashing problem from deep
reinforcement learning perspective. The main contributions of our proposed
DRLIH approach can be summarized as follows: (1) We propose a deep
reinforcement learning hashing network. In the proposed network, we utilize
recurrent neural network (RNN) as agents to model the hashing functions, which
take actions of projecting images into binary codes sequentially, so that the
current hashing function learning can take previous hashing functions' error
into account. (2) We propose a sequential learning strategy based on proposed
DRLIH. We define the state as a tuple of internal features of RNN's hidden
layers and image features, which can reflect history decisions made by the
agents. We also propose an action group method to enhance the correlation of
hash functions in the same group. Experiments on three widely-used datasets
demonstrate the effectiveness of our proposed DRLIH approach.
