Supplement to the paper: Supporting the analysis of a large coin hoard with AI-based methods.
Authors/Creators
- 1. Goethe Universität Frankfurt am Main - Germany
- 2. Römisch-Germanische Kommission – Germany
Description
ClaReNet is a joint project of the Römisch-Germanische Kommission (German Archaeological Institute) and the Big Data Lab (Goethe University Frankfurt), funded by the German Federal Ministry of Education and Research (BMBF). It tests the possibilities and limits of new digital methods of classification and representation. This supplement provides a snapshot of the methods used in the project, which will be published in the paper "Supporting the analysis of a large coin hoard with AI-based methods'' submitted to CAA 2023, and is based on the Github repository "https://github.com/Frankfurt-BigDataLab/2023_CAA_ClaReNet". The core of the analysis were digital images of around 70,000 coins discovered in a coin hoard at Le Câtillon II on the island of Jersey (UK), and the methods used fall into four parts:
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Object detection: cropping the image to the coin and reducing bias
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Unsupervised learning: observing the way the coins are grouped independently of domain information
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Supervised learning:combining the results of unsupervised learning and domain information
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Image matching - detecting similar dies.
The snapshot is used to show and reproduce the analysis at the time of the paper. Further information and sources can be found on the official Github repository.
Files
2023_CAA_ClaReNet-1.0.0.zip
Files
(4.5 MB)
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Additional details
Related works
- Is supplement to
- Dataset: 10.34780/kzw0-r608 (DOI)
References
- Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze. "Deep Clustering for Unsupervised Learning of Visual Features." Proc. ECCV (2018).
- Heinecke, Andreas, Emanuel Mayer, Abhinav Natarajan, & Yoonju Jung (2021). Unsupervised Statistical Learning for Die Analysis in Ancient Numismatics. CoRR, abs/2112.00290.
- Taylor, Zachary McCord, "The Computer-Aided Die Study (CADS): A Tool for Conducting Numismatic Die Studies with Computer Vision and Hierarchical Clustering" (2020). Computer Science Honors Theses. 54.
- Simonyan, K., & Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. CoRR, abs/1409.1556.
- Kampel, Martin, Maia Zaharieva, Recognizing Ancient Coins Based on Local Features. In: G. Bebis/R. Boyle/B. Parvin (Hrsg.), Advances in Visual Computing. ISVC 2008. Lecture Notes in Computer Science, vol 5358. (Berlin / Heidelberg 2008) 11-22. https://doi.org/10.1007/978-3-540-89639-5_2
- Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, Dhruv Batra. 2019. Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization. https://doi.org/10.1007/s11263-019-01228-7
- Rublee, Ethan, Vincent Rabaud, Kurt Konolige and Gary Bradski. 2011. ORB: An efficient alternative to SIFT or SURF. 2011 International Conference on Computer Vision, Barcelona, Spain, pp. 2564-2571, doi: https://doi.org/10.1109/ICCV.2011.6126544.