Machine Vision and Applications

, Volume 25, Issue 6, pp 1385–1397 | Cite as

Painting-91: a large scale database for computational painting categorization

  • Fahad Shahbaz Khan
  • Shida Beigpour
  • Joost van de Weijer
  • Michael Felsberg
Original Paper


Computer analysis of visual art, especially paintings, is an interesting cross-disciplinary research domain. Most of the research in the analysis of paintings 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 applications of our dataset namely: artist categorization, style classification and saliency detection. We investigate how local and global features popular in image classification perform for the tasks of artist and style categorization. For both categorization tasks, our experimental results suggest that combining multiple features 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 estimation algorithms.


Painting categorization Visual features Image classification 



This work has been supported by SSF through a grant for the project CUAS, by VR through a grant for the project ETT, through the Strategic Area for ICT research ELLIIT, and CADICS.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Fahad Shahbaz Khan
    • 1
  • Shida Beigpour
    • 2
  • Joost van de Weijer
    • 3
  • Michael Felsberg
    • 1
  1. 1.Computer Vision LaboratoryLinköping UniversityLinköpingSweden
  2. 2.Norwegian Colour and Visual Computing LaboratoryGjovik University CollegeGjøvikNorway
  3. 3.Computer Vision Centre BarcelonaUniversitat Autonoma de BarcelonaCataloniaSpain

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