Content-based image retrieval (CBIR) aims to display, as a result of a search, images with the same visual contents as a query. This problem has attracted increasing attention in the area of computer vision. Learning-based hashing techniques are amongst the most studied search approaches for approximate nearest neighbors in large-scale image retrieval. With the advance of deep neural networks in image representation, hashing methods for CBIR have started using deep learning to build binary codes. Such strategies are generally known as deep hashing techniques. In this paper, we present a comprehensive deep hashing survey for the task of image retrieval with multiple labels, categorizing the methods according to how the input images are treated: pointwise, pairwise, tripletwise and listwise, as well as their relationships. In addition, we present discussions regarding the cost of space, efficiency and search quality of the described models, as well as open issues and future work opportunities.

中文翻译：

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多标签图像检索的深度散列：一项调查

基于内容的图像检索 (CBIR) 旨在作为搜索结果显示与查询具有相同视觉内容的图像。这个问题在计算机视觉领域引起了越来越多的关注。基于学习的散列技术是大规模图像检索中研究最多的近似最近邻搜索方法之一。随着深度神经网络在图像表示方面的进步，CBIR 的哈希方法已经开始使用深度学习来构建二进制代码。这种策略通常被称为深度散列技术。在本文中，我们对具有多个标签的图像检索任务进行了全面的深度散列调查，根据输入图像的处理方式对方法进行分类：逐点、成对、三元组和列表，以及它们的关系。此外，