Deep Supervised Hashing leveraging Quadratic Spherical Mutual Information for Content-based Image Retrieval
This work addresses the need for efficient and accurate large-scale image retrieval, offering a structured approach to improve hashing techniques, though it is incremental in nature.
The paper tackles the problem of suboptimal results in deep supervised hashing for content-based image retrieval by proposing a method that optimizes learned codes using Quadratic Spherical Mutual Information, achieving state-of-the-art performance in various scenarios.
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.