Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/s00138-014-0621-6 | Machine Vision and Applications |
Keywords | Field | DocType |
Painting categorization,Visual features,Image classification | Computer vision,Categorization,Computer science,Salience (neuroscience),Painting,Computer analysis,Artificial intelligence,Contextual image classification,Machine learning | Journal |
Volume | Issue | ISSN |
25 | 6 | 0932-8092 |
Citations | PageRank | References |
29 | 0.86 | 41 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fahad Shahbaz Khan | 1 | 1622 | 69.24 |
| Shida Beigpour | 2 | 29 | 0.86 |
| Joost van de Weijer | 3 | 2117 | 124.82 |
| Michael Felsberg | 4 | 2419 | 130.29 |