Unsupervised affinity learning based on manifold analysis for image retrieval: A survey

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 mac...

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Bibliographic Details
Published in:	Computer science review Vol. 53; p. 100657
Main Authors:	Pereira-Ferrero, V.H., Lewis, T.G., Valem, L.P., Ferrero, L.G.P., Pedronette, D.C.G., Latecki, L.J.
Format:	Journal Article
Language:	English
Published:	Elsevier Inc 01-08-2024
Subjects:	Affinity learning Diffusion process Image retrieval Manifold learning Multimedia retrieval Ranking Unsupervised Affinity learning Ranking Image retrieval Diffusion process Unsupervised Manifold learning Multimedia retrieval
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Summary:	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. •Comprehensive survey on unsupervised post-processing methods for image retrieval.•Discussion including a brief history, organization, and evolution of the area.•Analysis of literature trends through keywords network analysis.•A systematic review including organization and summarization of the main works.•Experimental study involving various unsupervised methods, features, and datasets.
ISSN:	1574-0137
DOI:	10.1016/j.cosrev.2024.100657