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.- Anthology ID:
- 2020.ai4hi-1.4
- Volume:
- Proceedings of the 1st International Workshop on Artificial Intelligence for Historical Image Enrichment and Access
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Editors:
- Yalemisew Abgaz, Amelie Dorn, Jose Luis Preza Diaz, Gerda Koch
- Venue:
- AI4HI
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- 23–28
- Language:
- English
- URL:
- https://aclanthology.org/2020.ai4hi-1.4
- DOI:
- Cite (ACL):
- Christian Bartz, Nitisha Jain, and Ralf Krestel. 2020. Automatic Matching of Paintings and Descriptions in Art-Historic Archives using Multimodal Analysis. In Proceedings of the 1st International Workshop on Artificial Intelligence for Historical Image Enrichment and Access, pages 23–28, Marseille, France. European Language Resources Association (ELRA).
- Cite (Informal):
- Automatic Matching of Paintings and Descriptions in Art-Historic Archives using Multimodal Analysis (Bartz et al., AI4HI 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.ai4hi-1.4.pdf