  Deep hashing has shown promising results in image retrieval and recognition.
Despite its success, most existing deep hashing approaches are rather similar:
either multi-layer perceptron or CNN is applied to extract image feature,
followed by different binarization activation functions such as sigmoid, tanh
or autoencoder to generate binary code. In this work, we introduce a novel
decision-making approach for deep supervised hashing. We formulate the hashing
problem as travelling across the vertices in the binary code space, and learn a
deep Q-network with a novel label embedding reward defined by
Bose-Chaudhuri-Hocquenghem (BCH) codes to explore the best path. Extensive
experiments and analysis on the CIFAR-10 and NUS-WIDE dataset show that our
approach outperforms state-of-the-art supervised hashing methods under various
code lengths.
