Despite the advances in machine learning techniques, similarity assessment among multimedia data remains a challenging task of broad interest in computer science. Substantial progress has been achieved in acquiring meaningful data representations, but how to compare them, plays a pivotal role in machine learning and retrieval tasks. Traditional pairwise measures are widely used, yet unsupervised affinity learning approaches have emerged as a valuable solution for enhancing retrieval effectiveness. These methods leverage the dataset manifold to encode contextual information, refining initial similarity/dissimilarity measures through post-processing. In other words, measuring the similarity between data objects within the context of other data objects is often more effective. This survey provides a comprehensive discussion about unsupervised post-processing methods, addressing the historical development and proposing an organization of the area, with a specific emphasis on image retrieval. A systematic review was conducted contributing to a formal understanding of the field. Additionally, an experimental study is presented to evaluate the potential of such methods in improving retrieval results, focusing on recent features extracted from Convolutional Neural Networks (CNNs) and Transformer models, in 8 distinct datasets, and over 329.877 images analyzed. State-of-the-art comparison for Flowers, Corel5k, and ALOI datasets, the Rank Flow Embedding method outperformed all state-of-art approaches, achieving 99.65%, 96.79%, and 97.73%, respectively.

中文翻译：

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基于图像检索流形分析的无监督亲和学习：一项调查


尽管机器学习技术取得了进步，多媒体数据之间的相似性评估仍然是计算机科学中广泛关注的一项具有挑战性的任务。在获取有意义的数据表示方面已经取得了实质性进展，但如何比较它们在机器学习和检索任务中起着关键作用。传统的成对测量被广泛使用，但无监督的亲和学习方法已成为提高检索效率的有价值的解决方案。这些方法利用数据集流形对上下文信息进行编码，通过后处理细化初始相似性/相异性度量。换句话说，测量其他数据对象上下文中的数据对象之间的相似性通常更有效。这项调查对无监督后处理方法进行了全面的讨论，讨论了历史发展并提出了该领域的组织，特别强调图像检索。进行了系统审查，有助于对该领域的正式了解。此外，还提出了一项实验研究，以评估此类方法在改善检索结果方面的潜力，重点关注从 8 个不同数据集中的卷积神经网络 (CNN) 和 Transformer 模型中提取的最新特征，并分析了超过 329.877 张图像。对 Flowers、Corel5k 和 ALOI 数据集进行最先进的比较，Rank Flow Embedding 方法优于所有最先进的方法，分别达到 99.65%、96.79% 和 97.73%。