  Several deep supervised hashing techniques have been proposed to allow for
efficiently querying large image databases. However, deep supervised image
hashing techniques are developed, to a great extent, heuristically often
leading to suboptimal results. Contrary to this, we propose an efficient deep
supervised hashing algorithm that optimizes the learned codes using an
information-theoretic measure, the Quadratic Mutual Information (QMI). The
proposed method is adapted to the needs of large-scale hashing and information
retrieval leading to a novel information-theoretic measure, the Quadratic
Spherical Mutual Information (QSMI). Apart from demonstrating the effectiveness
of the proposed method under different scenarios and outperforming existing
state-of-the-art image hashing techniques, this paper provides a structured way
to model the process of information retrieval and develop novel methods adapted
to the needs of each application.
