Visual Link Retrieval in a Database of Paintings
Abstract
This paper examines how far state-of-the-art machine vision algorithms can be used to retrieve common visual patterns shared by series of paintings. The research of such visual patterns, central to Art History Research, is challenging because of the diversity of similarity criteria that could relevantly demonstrate genealogical links. We design a methodology and a tool to annotate efficiently clusters of similar paintings and test various algorithms in a retrieval task. We show that pre-trained convolutional neural network can perform better for this task than other machine vision methods aimed at photograph analysis. We also show that retrieval performance can be significantly improved by fine-tuning a network specifically for this task.
Cite
Text
Seguin et al. "Visual Link Retrieval in a Database of Paintings." European Conference on Computer Vision Workshops, 2016. doi:10.1007/978-3-319-46604-0_52Markdown
[Seguin et al. "Visual Link Retrieval in a Database of Paintings." European Conference on Computer Vision Workshops, 2016.](https://mlanthology.org/eccvw/2016/seguin2016eccvw-visual/) doi:10.1007/978-3-319-46604-0_52BibTeX
@inproceedings{seguin2016eccvw-visual,
title = {{Visual Link Retrieval in a Database of Paintings}},
author = {Seguin, Benoit and Striolo, Carlotta and diLenardo, Isabella and Kaplan, Frédéric},
booktitle = {European Conference on Computer Vision Workshops},
year = {2016},
pages = {753-767},
doi = {10.1007/978-3-319-46604-0_52},
url = {https://mlanthology.org/eccvw/2016/seguin2016eccvw-visual/}
}