The large and growing amount of digital data creates a pressing need for approaches capable of indexing and retrieving multimedia content. A traditional and fundamental challenge consists of effectively and efficiently performing nearest-neighbor searches. After decades of research, several different methods are available, including trees, hashing, and graph-based approaches. Most of the current methods exploit learning to hash approaches based on deep learning. In spite of effective results and compact codes obtained, such methods often require a significant amount of labeled data for training. Unsupervised approaches also rely on expensive training procedures usually based on a huge amount of data. In this work, we propose an unsupervised data-independent approach for nearest neighbor searches, which can be used with different features, including deep features trained by transfer learning. The method uses a rank-based formulation and exploits a hashing approach for efficient ranked list computation at query time. A comprehensive experimental evaluation was conducted on 7 public datasets, considering deep features based on CNNs and Transformers. Both effectiveness and efficiency aspects were evaluated. The proposed approach achieves remarkable results in comparison to traditional and state-of-the-art methods. Hence, it is an attractive and innovative solution, especially when costly training procedures need to be avoided.

大量且不断增长的数字数据迫切需要能够索引和检索多媒体内容的方法。传统且基本的挑战包括有效且高效地执行最近邻搜索。经过几十年的研究，出现了几种不同的方法，包括树、散列和基于图的方法。当前的大多数方法都利用基于深度学习的学习来哈希方法。尽管获得了有效的结果和紧凑的代码，但此类方法通常需要大量的标记数据进行训练。无监督方法还依赖于通常基于大量数据的昂贵训练程序。在这项工作中，我们提出了一种用于最近邻搜索的无监督数据独立方法，该方法可以与不同的特征一起使用，包括通过迁移学习训练的深层特征。该方法使用基于排名的公式并利用散列方法在查询时进行高效的排名列表计算。对 7 个公共数据集进行了全面的实验评估，考虑了基于 CNN 和 Transformer 的深层特征。有效性和效率方面都进行了评估。与传统和最先进的方法相比，所提出的方法取得了显着的结果。因此，它是一种有吸引力的创新解决方案，特别是当需要避免昂贵的培训程序时。