CAA2023 Conference Proceedings

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Published August 30, 2023 | Version v2
Conference paper Open

Supporting the analysis of a large coin hoard with AI-based methods

Creators

  • 1. Goethe Universität Frankfurt am Main - Germany
  • 2. Römisch-Germanische Kommission – Germany

Description

In our project "Classifications and Representations for Networks: From types and characteristics to linked open data for Celtic coinages" (ClaReNet) we had image data for one of the largest Celtic coin hoards ever found: Le Câtillon II with nearly 70,000 coins. In the initial stages of our approach, the main problem was how to deal with the dataset without having any information about it. First, we separated the dataset into groups of coins of different sizes using object recognition combined with the scale contained in the images. The main approach was to treat the coins independently of the underlying classification and analyse how an unsupervised method could group them. We later evaluated our results against the table provided and produced by the expert team. In addition, we have reviewed these expert classifications to improve them and provide a quality check, but also to get a better understanding of how the experts classify the coins, especially for those in poor condition. Additionally, we  took a closer look at a single class and tried to identify coins struck with the different dies used. The phases of our work have been presented at CAA 2022 in Oxford and at CAA 2023 in Amsterdam.

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Related works

Is supplemented by
Software: 10.5281/zenodo.8301158 (DOI)

References

  • Bay, Herbert, Tinne Tuytelaars, Luc Van Gool. 2006. SURF: Speeded Up Robust Features. In: Leonardis, A., Bischof, H., Pinz, A. (eds) Computer Vision – ECCV 2006. ECCV 2006. Lecture Notes in Computer Science, vol 3951. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11744023_3
  • Caron, Mathilde, Piotr Bojanowski, Armand Joulin, Matthijs Douze. 2018. Deep Clustering for Unsupervised Learning of Visual Features. https://doi.org/10.48550/arXiv.1807.0552
  • Colbert de Beaulieu, Jean-Baptiste. 1957. Le trésor de Jersey-11 et la numismatique celtique des deux Bretagnes. Revue Belge de Numismatique Vol. 103. 47-88.
  • Duan, Kaiwen, Song Bai, Lingxi Xie, Honggang Qi, Qingming Huang and Qi Tian. 2019. CenterNet: Keypoint Triplets for Object Detection. https://doi.org/10.48550/arXiv.1611.10012
  • Heinecke, Andreas, Emanuel Mayer, Abhinav Natarajan, Yoonju Jung, Unsupervised Statistical Learning for Die Analysis in Ancient Numismatics. Computer Vision and Pattern Recognition 2021. https://doi.org/10.48550/arXiv.2112.0290
  • Huang, Jonathan, Vivek Rathod, Chen Sun, Menglong Zhu, Anoop Korattikara, Alireza Fathi, Ian Fischer, Zbigniew Wojna, Yang Song, Sergio Guadarrama and Kevin Murphy. 2017. Speed/accuracy trade-offs for modern convolutional object detectors. https://doi.org/10.48550/arXiv.1611.10012
  • 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
  • Karami, Ebrahim, Siva Prasad, Mohamed Shehata. 2017. Image Matching Using SIFT, SURF, BRIEF and ORB: Performance Comparison for Distorted Images. https://doi.org/10.48550/arXiv.1710.02726
  • Lowe, David G. 2017.Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60, 91–110. https://doi.org/10.1023/B:VISI.0000029664.99615.94
  • Lundberg Scott M., Su-In Lee. 2017. A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems 30 (NIPS 2017)
  • 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
  • Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. Why should i trust you?: Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2016.
  • 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.
  • 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.
  • Tzutalin. LabelImg. Git code (2015).