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.

这篇论文研究了利用人工智能和神经网络技术实现数字化艺术作品的分类和管理，以及通过使用迁移学习和机器学习方法实现画家的自动识别和分类，实现了 85% 的分类准确率。