EnsArtNet: Ensemble neural network architecture for identifying art styles from paintings

• This work proposes EnsArtNet: the model of neural network with several feature extractors. • EnsArtNet distinguishes between the styles of the artists’ paintings with high accuracy. • The model objectively measures the similarity with the other artists’ styles. • A complex neural network architecture is efficient in this field of research. • The achieved accuracies on two datasets are 84.93% and 86.65%. The digitization of paintings offers many benefits and opportunities for artists, collectors, and the public. It opens possibilities for researchers to investigate new hidden patterns that were not obvious to experts before. This work aims to develop a methodology that can identify and compare painting styles from various famous painters, such as Vincent van Gogh, Pablo Picasso, Claude Monet, and others, using an ensemble convolutional neural network (CNN). Our approach, named EnsArtNet, can distinguish between the styles of the artists’ paintings with high accuracy and objectively measure the similarity with the other artists’ styles. The proposed model was compared to several other state-of-the-art neural network architectures , and we show that EnsArtNet performs better than the compared one. Our model gives promising accuracy on two large-scale datasets: 84.93% on the WikiArt dataset and 86.65% on the Best Artworks of All Time dataset, which is better by more than 6% compared to other evaluated architectures. In this work, we also showed that a complex neural network architecture is efficient in this field of research, and an explanation using the GradCAM method supported it. Our methodology can help art researchers and enthusiasts analyze paintings’ stylistic features and similarities and appreciate the creativity and diversity of visual arts.