Synergy of Machine and Deep Learning Models for Multi-Painter Recognition
This addresses the problem of categorizing large art collections for researchers and curators, but it is incremental as it combines existing methods on a new dataset.
The paper tackled painter recognition in digitized paintings from WikiArt by using transfer learning for feature extraction and classical machine learning for classification, achieving up to 85% accuracy with RegNet and SVM. They also introduced a new dataset of 62 artists.
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.