Abstract
In recent years, a range of problems within the broad umbrella of automatic, computer vision based analysis of ancient coins has been attracting an increasing amount of attention. Notwithstanding this research effort, the results achieved by the state of the art in the published literature remain poor and far from sufficiently well performing for any practical purpose. In the present paper we present a series of contributions which we believe will benefit the interested community. Firstly, we explain that the approach of visual matching of coins, universally adopted in all existing published papers on the topic, is not of practical interest because the number of ancient coin types exceeds by far the number of those types which have been imaged, be it in digital form (e.g. online) or otherwise (traditional film, in print, etc.). Rather, we argue that the focus should be on the understanding of the semantic content of coins. Hence, we describe a novel method which uses real-world multimodal input to extract and associate semantic concepts with the correct coin images and then using a novel convolutional neural network learn the appearance of these concepts. Empirical evidence on a real-world and by far the largest data set of ancient coins, we demonstrate highly promising results.
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References
Agarap, A.F.: Deep learning using rectified linear units (ReLU). arXiv:1803.08375 (2018)
Anwar, H., Zambanini, S., Kampel, M.: Supporting ancient coin classification by image-based reverse side symbol recognition. In: Proceedings of the International Conference on Computer Analysis of Images and Patterns, pp. 17–25 (2013)
Anwar, H., Zambanini, S., Kampel, M.: Coarse-grained ancient coin classification using image-based reverse side motif recognition. Mach. Vis. Appl. 26(2), 295–304 (2015)
Arandjelović, O.: Automatic attribution of ancient Roman imperial coins. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1728–1734 (2010)
Arandjelović, O.: Reading ancient coins: automatically identifying denarii using obverse legend seeded retrieval. In: Proceedings of the European Conference on Computer Vision, vol. 4, pp. 317–330 (2012)
Conn, B., Arandjelović, O.: Towards computer vision based ancient coin recognition in the wild – automatic reliable image preprocessing and normalization. In: Proceedings of the IEEE International Joint Conference on Neural Networks, pp. 1457–1464 (2017)
Fare, C., Arandjelović, O.: Ancient Roman coin retrieval: a new dataset and a systematic examination of the effects of coin grade. In: Proceedings of the European Conference on Information Retrieval, pp. 410–423 (2017)
Janocha, K., Czarnecki, W.M.: On loss functions for deep neural networks in classification. arXiv:1702.05659 (2017)
Kampel, M., Zaharieva, M.: Recognizing ancient coins based on local features. In: Proceedings of the International Symposium on Visual Computing, vol. 1, pp. 11–22 (2008)
Kinga, D., Adam, J.B.: A method for stochastic optimization. In: Proceedings of the International Conference on Learning Representations, vol. 5 (2015)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Mattingly, H.: The Roman Imperial Coinage, vol. 7. Spink, London (1966)
Rieutort-Louis, W., Arandjelović, O.: Bo(V)W models for object recognition from video. In: Proceedings of the International Conference on Systems, Signals and Image Processing, pp. 89–92 (2015)
Rieutort-Louis, W., Arandjelović, O.: Description transition tables for object retrieval using unconstrained cluttered video acquired using a consumer level handheld mobile device. In: Proceedings of the IEEE International Joint Conference on Neural Networks, pp. 3030–3037 (2016)
Schlag, I., Arandjelović, O.: Ancient Roman coin recognition in the wild using deep learning based recognition of artistically depicted face profiles. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2898–2906 (2017)
Yue, X., Dimitriou, N., Arandjelović, O.: Colorectal cancer outcome prediction from H&E whole slide images using machine learning and automatically inferred phenotype profiles. In: Proceedings of the International Conference on Bioinformatics and Computational Biology (2019)
Zaharieva, M., Kampel, M., Zambanini, S.: Image based recognition of ancient coins. In: Proceedings of the International Conference on Computer Analysis of Images and Patterns, pp. 547–554 (2007)
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Cooper, J., Arandjelović, O. (2020). Understanding Ancient Coin Images. In: Oneto, L., Navarin, N., Sperduti, A., Anguita, D. (eds) Recent Advances in Big Data and Deep Learning. INNSBDDL 2019. Proceedings of the International Neural Networks Society, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-030-16841-4_34
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DOI: https://doi.org/10.1007/978-3-030-16841-4_34
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