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Several deep supervised hashing techniques have been proposed to allow forefficiently querying large image databases. However, deep supervised imagehashing techniques are developed, to a great extent, heuristically oftenleading to suboptimal results. Contrary to this, we propose an efficient deepsupervised hashing algorithm that optimizes the learned codes using aninformation-theoretic measure, the Quadratic Mutual Information (QMI). Theproposed method is adapted to the needs of large-scale hashing and informationretrieval leading to a novel information-theoretic measure, the QuadraticSpherical Mutual Information (QSMI). Apart from demonstrating the effectivenessof the proposed method under different scenarios and outperforming existingstate-of-the-art image hashing techniques, this paper provides a structured wayto model the process of information retrieval and develop novel methods adaptedto the needs of each application.

Deep Supervised Hashing leveraging Quadratic Spherical Mutual Information for Content-based Image Retrieval