Abstract Hashing methods have been widely applied to approximate nearest neighbor search in large-scale image retrieval due to its fast search speed and efficient storage space. In practice, most images are with multiple category-aware objects, i.e., multi-label images. This paper focuses on hash code learning for multi-label image retrieval. Most existing hashing methods directly extract one patch such as a downsized crop from each image as a training example, which ignores the multi-label characteristic of images and leads to suboptimal representations for multi-label images. Some researches have proved that each multi-label image follows a multi-instance assumption, where each image is represented as a bag of category-aware proposals (instances). However, existing multiple-instance learning methods use predefined statistical functions with limited learning capability to construct bag features, and they are designed for classification or pairwise-similarity preserving. Thus, directly applying existing multiple-instance learning methods into deep hashing framework still leads to suboptimal hash codes for retrieval. In this paper, we pose hashing learning for multi-label image retrieval as a problem of multiple-instance ranking learning. To solve this problem, we present an end-to-end deep hashing framework, referred to as Deep Multiple-Instance Ranking based Hashing (DMIRH). In DMIRH, we design a category-aware bag feature construction module, which jointly assigns the learned instances into categories and aggregates the selected instance features into a bag feature representation that can capture the multi-label information of each image. In addition, we propose a novel learning objective, which consists of an Inner Product distance based quantization loss to control the hash quality and a listwise ranking loss to preserve the ranking relationships. Experimental results on public benchmarks show the superiority of DMIRH over several state-of-the-art hashing methods.

中文翻译：

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基于多实例排序的多标签图像检索深度散列

摘要 散列方法因其搜索速度快、存储空间高效等优点，被广泛应用于大规模图像检索中的近似最近邻搜索。在实践中，大多数图像具有多个类别感知对象，即多标签图像。本文重点研究多标签图像检索的哈希码学习。大多数现有的散列方法直接从每张图像中提取一个补丁（例如缩小的裁剪）作为训练示例，这忽略了图像的多标签特征并导致多标签图像的次优表示。一些研究证明，每张多标签图像都遵循多实例假设，其中每张图像都表示为一袋类别感知建议（实例）。然而，现有的多实例学习方法使用具有有限学习能力的预定义统计函数来构建袋子特征，并且它们被设计用于分类或成对相似性保留。因此，将现有的多实例学习方法直接应用于深度哈希框架仍然会导致用于检索的次优哈希码。在本文中，我们将多标签图像检索的哈希学习作为多实例排序学习的一个问题。为了解决这个问题，我们提出了一个端到端的深度散列框架，称为基于深度多实例排名的散列（DMIRH）。在DMIRH中，我们设计了一个category-aware bag特征构建模块，它联合将学习到的实例分配到类别中，并将选定的实例特征聚合成一个包特征表示，该表示可以捕获每个图像的多标签信息。此外，我们提出了一个新的学习目标，它包括一个基于内积距离的量化损失来控制哈希质量和一个列表排名损失来保持排名关系。公共基准的实验结果表明 DMIRH 优于几种最先进的散列方法。