@inproceedings{2c0ced51d2384f0a830b6f50823cc85b,
title = "Learning Algorithms for Digital Reconstruction of Van Gogh{\textquoteright}s Drawings",
abstract = "Many works of Van Gogh{\textquoteright}s oeuvre, such as letters, drawings and paintings, have been severely degraded due to light exposure. Digital reconstruction of faded color can help to envisage how the artist{\textquoteright}s work may have looked at the time of creation. In this paper, we study the reconstruction of Vincent van Gogh{\textquoteright}s drawings by means of learning schemes and on the basis of the available reproductions of these drawings. In particular, we investigate the use of three machine learning algorithms, k-nearest neighbor (kNN) estimation, linear regression (LR), and convolutional neural networks (CNN), for learning the reconstruction of these faded drawings. Experimental results show that the reconstruction performance of the kNN method is slightly better than those of the CNN. The reconstruction performance of the LR is much worse than those of the kNN and the CNN.",
keywords = "Van Gogh{\textquoteright}s drawing, Drawing reconstruction, Reproduction, Machine learning",
author = "Yuan Zeng and Jiexiong Tang and {van der Lubbe}, Jan and Marco Loog",
year = "2016",
doi = "10.1007/978-3-319-48496-9_26",
language = "English",
isbn = "978-3-319-48495-2",
volume = "1",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "322--333",
editor = "Marinos Ioannides and Eleanor Vink and Antonia Moropoulou and Monika Hagedorn-Saupe and Antonella Fresa and Gunnar Liest{\o}l and Vlatka Rajcic and Pierre Grussenmeyer",
booktitle = "EuroMed 2016 - 6th International Conference - Proceedings",
note = "EuroMed 2016 : International Conference on Digital Heritage ; Conference date: 31-10-2016 Through 05-11-2016",
}