Several deep supervised hashing techniques have been proposed to allow for extracting compact and efficient neural network representations for various tasks. However, many deep supervised hashing techniques ignore several information-theoretic aspects of the process of information retrieval, often leading to sub-optimal results. In this paper, we propose an efficient deep supervised hashing algorithm that optimizes the learned compact codes using an information-theoretic measure, the Quadratic Mutual Information (QMI). The proposed method is adapted to the needs of efficient image 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 different applications.

已经提出了几种深度监督的哈希技术，以允许提取用于各种任务的紧凑而有效的神经网络表示。但是，许多深度监督的哈希技术忽略了信息检索过程中的几个信息理论方面，通常会导致结果不理想。在本文中，我们提出了一种有效的深度监督哈希算法，该算法使用信息理论量度二次互信息（QMI）优化学习的紧凑代码。所提出的方法适合于有效的图像散列和信息检索的需求，从而产生了一种新颖的信息理论方法-二次球面互信息（QSMI）。