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

TL;DR

This paper introduces a novel deep supervised hashing method that uses Quadratic Spherical Mutual Information to optimize image codes, improving large-scale image retrieval performance over existing techniques.

Contribution

It presents a new information-theoretic hashing algorithm based on QSMI, tailored for large-scale image retrieval, outperforming state-of-the-art methods.

Findings

Outperforms existing hashing techniques in various scenarios

Demonstrates the effectiveness of QSMI in image retrieval

Provides a structured approach to model information retrieval processes

Abstract

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.