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arXiv 1901.051352019-01-16cs.CV

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

Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）

Nikolaos Passalis, Anastasios Tefas

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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