@inproceedings{bartz-etal-2020-automatic,
title = "Automatic Matching of Paintings and Descriptions in Art-Historic Archives using Multimodal Analysis",
author = "Bartz, Christian and
Jain, Nitisha and
Krestel, Ralf",
editor = "Abgaz, Yalemisew and
Dorn, Amelie and
Diaz, Jose Luis Preza and
Koch, Gerda",
booktitle = "Proceedings of the 1st International Workshop on Artificial Intelligence for Historical Image Enrichment and Access",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/2020.ai4hi-1.4",
pages = "23--28",
abstract = "Cultural heritage data plays a pivotal role in the understanding of human history and culture. A wealth of information is buried in art-historic archives which can be extracted via digitization and analysis. This information can facilitate search and browsing, help art historians to track the provenance of artworks and enable wider semantic text exploration for digital cultural resources. However, this information is contained in images of artworks, as well as textual descriptions or annotations accompanied with the images. During the digitization of such resources, the valuable associations between the images and texts are frequently lost. In this project description, we propose an approach to retrieve the associations between images and texts for artworks from art-historic archives. To this end, we use machine learning to generate text descriptions for the extracted images on the one hand, and to detect descriptive phrases and titles of images from the text on the other hand. Finally, we use embeddings to align both, the descriptions and the images.",
language = "English",
ISBN = "979-10-95546-63-4",
}
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<abstract>Cultural heritage data plays a pivotal role in the understanding of human history and culture. A wealth of information is buried in art-historic archives which can be extracted via digitization and analysis. This information can facilitate search and browsing, help art historians to track the provenance of artworks and enable wider semantic text exploration for digital cultural resources. However, this information is contained in images of artworks, as well as textual descriptions or annotations accompanied with the images. During the digitization of such resources, the valuable associations between the images and texts are frequently lost. In this project description, we propose an approach to retrieve the associations between images and texts for artworks from art-historic archives. To this end, we use machine learning to generate text descriptions for the extracted images on the one hand, and to detect descriptive phrases and titles of images from the text on the other hand. Finally, we use embeddings to align both, the descriptions and the images.</abstract>
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%0 Conference Proceedings
%T Automatic Matching of Paintings and Descriptions in Art-Historic Archives using Multimodal Analysis
%A Bartz, Christian
%A Jain, Nitisha
%A Krestel, Ralf
%Y Abgaz, Yalemisew
%Y Dorn, Amelie
%Y Diaz, Jose Luis Preza
%Y Koch, Gerda
%S Proceedings of the 1st International Workshop on Artificial Intelligence for Historical Image Enrichment and Access
%D 2020
%8 May
%I European Language Resources Association (ELRA)
%C Marseille, France
%@ 979-10-95546-63-4
%G English
%F bartz-etal-2020-automatic
%X Cultural heritage data plays a pivotal role in the understanding of human history and culture. A wealth of information is buried in art-historic archives which can be extracted via digitization and analysis. This information can facilitate search and browsing, help art historians to track the provenance of artworks and enable wider semantic text exploration for digital cultural resources. However, this information is contained in images of artworks, as well as textual descriptions or annotations accompanied with the images. During the digitization of such resources, the valuable associations between the images and texts are frequently lost. In this project description, we propose an approach to retrieve the associations between images and texts for artworks from art-historic archives. To this end, we use machine learning to generate text descriptions for the extracted images on the one hand, and to detect descriptive phrases and titles of images from the text on the other hand. Finally, we use embeddings to align both, the descriptions and the images.
%U https://aclanthology.org/2020.ai4hi-1.4
%P 23-28
Markdown (Informal)
[Automatic Matching of Paintings and Descriptions in Art-Historic Archives using Multimodal Analysis](https://aclanthology.org/2020.ai4hi-1.4) (Bartz et al., AI4HI 2020)
ACL