Art Style Classification with Self-Trained Ensemble of AutoEncoding Transformations
This work provides a significant improvement in art style classification, benefiting art historians and digital art archives by enabling more accurate and efficient indexing of artworks.
This paper addresses the problem of art style classification, which is crucial for indexing large art databases. The authors achieved a nearly 20% improvement over existing approaches on the WikiArt dataset, which contains 27 art categories.
The artistic style of a painting is a rich descriptor that reveals both visual and deep intrinsic knowledge about how an artist uniquely portrays and expresses their creative vision. Accurate categorization of paintings across different artistic movements and styles is critical for large-scale indexing of art databases. However, the automatic extraction and recognition of these highly dense artistic features has received little to no attention in the field of computer vision research. In this paper, we investigate the use of deep self-supervised learning methods to solve the problem of recognizing complex artistic styles with high intra-class and low inter-class variation. Further, we outperform existing approaches by almost 20% on a highly class imbalanced WikiArt dataset with 27 art categories. To achieve this, we train the EnAET semi-supervised learning model (Wang et al., 2019) with limited annotated data samples and supplement it with self-supervised representations learned from an ensemble of spatial and non-spatial transformations.