Noname manuscript No. (will be inserted by the editor) Painting-91: A Large Scale Database for Computational Painting Categorization

Abstract Computer analysis of visual art, especially paintings, is an interesting cross-disciplinary research domain. Most of the research in the analysis of paint-ings involve medium to small range datasets with own specific settings. Interestingly, significant progress has been made in the field of object and scene recognition lately. A key factor in this success is the introduction and availability of benchmark datasets for evaluation. Surprisingly such a benchmark setup is still missing in the area of computational painting categorization. In this work, we propose a novel large scale dataset of digital paintings. The dataset consists of paintings from 91 different painters. We further show three ap-plications of our dataset namely: artist categorization, style classification and saliency detection. We investi-gate how local and global features popular in image classification perform for the tasks of artist and style categorization. For both categorization tasks, our ex-perimental results suggest that combining multiple fea-tures significantly improves the final performance. We show that state-of-the-art computer vision methods can correctly classify 50 % of unseen paintings to its painter in a large dataset and correctly attribute its artistic style in over 60 % of the cases. Additionally, we explore the task of saliency detection on paintings and show experimental findings using state-of-the-art saliency es-timation algorithms