An Overview of Machine Learning and Deep Learning Methods for Style Classification in Paintings

Dimitra G. Papadopoulou, Panagiotis D. Michailidis

The purpose of this review is to present an overview of artificial intelligence methods for classifying paintings into the artistic movement to which they belong. To achieve this goal, a literature review of research articles from the 2014–2024 period was carried out. The search for scientific articles was carried out in the Scopus database. The initial search yielded 492 publications and after successive stages of screening and full-text evaluation, 39 articles were finally selected for detailed analysis. The review presents (a) the datasets used in the works, (b) the range of artistic movements examined and (c) the computational methods from machine learning to deep neural networks and transfer learning. Methodological issues are highlighted, such as class imbalance of the samples, dataset bias and the limitations of commonly used evaluation metrics. The general finding is that a variety of methodologies were applied, with an increasing use of deep learning and transfer learning models, which in many cases are reported as effective within specific datasets and experimental protocols. Finally, the review offers a taxonomy of methodologies and maps trends and research gaps in research on painting style classification over the last decade, while at the same time making suggestions for future research.