  With the rapid growth of image and video data on the web, hashing has been
extensively studied for image or video search in recent years. Benefit from
recent advances in deep learning, deep hashing methods have achieved promising
results for image retrieval. However, there are some limitations of previous
deep hashing methods (e.g., the semantic information is not fully exploited).
In this paper, we develop a deep supervised discrete hashing algorithm based on
the assumption that the learned binary codes should be ideal for
classification. Both the pairwise label information and the classification
information are used to learn the hash codes within one stream framework. We
constrain the outputs of the last layer to be binary codes directly, which is
rarely investigated in deep hashing algorithm. Because of the discrete nature
of hash codes, an alternating minimization method is used to optimize the
objective function. Experimental results have shown that our method outperforms
current state-of-the-art methods on benchmark datasets.
