The growing availability of digitized art collections has created the need to manage, analyze and categorize large amounts of data related to abstract concepts, highlighting a demanding problem of computer science and leading to new research perspectives. Advances in artificial intelligence and neural networks provide the right tools for this challenge. The analysis of artworks to extract features useful in certain works is at the heart of the era. In the present work, we approach the problem of painter recognition in a set of digitized paintings, derived from the WikiArt repository, using transfer learning to extract the appropriate features and classical machine learning methods to evaluate the result. Through the testing of various models and their fine tuning we came to the conclusion that RegNet performs better in exporting features, while SVM makes the best classification of images based on the painter with a performance of up to 85%. Also, we introduced a new large dataset for painting recognition task including 62 artists achieving good results.

翻译：数字化艺术收藏品日益普及，催生了管理、分析和归类与抽象概念相关的大量数据的需求，这凸显了计算机科学领域的一个严峻问题，并开辟了新的研究视角。人工智能和神经网络的进步为此挑战提供了合适的工具。分析艺术作品以提取特定工作中有效的特征，已成为当今时代的核心。在本研究中，我们针对源自WikiArt存储库的一组数字化画作，采用迁移学习提取适当特征，并结合经典机器学习方法评估结果，来处理画家识别问题。通过测试多种模型并对其进行微调，我们得出结论：RegNet在特征导出方面表现更优，而SVM则基于画家信息实现最佳图像分类，性能高达85%。此外，我们引入了一个包含62位艺术家的大型新数据集，用于绘画识别任务，并取得了良好效果。