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Recognizing Artistic Style of Archaeological Image Fragments Using Deep Style Extrapolation

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Culture and Computing (HCII 2025)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15800))

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Abstract

Ancient artworks obtained in archaeological excavations usually suffer from a certain degree of fragmentation and physical degradation. Often, fragments of multiple artifacts from different periods or artistic styles could be found on the same site. With each fragment containing only partial information about its source, and pieces from different objects being mixed, categorizing broken artifacts based on their visual cues could be a challenging task, even for professionals. As classification is a common function of many machine learning models, the power of modern architectures can be harnessed for efficient and accurate fragment classification. In this work, we present a generalized deep-learning framework for predicting the artistic style of image fragments, achieving state-of-the-art results for pieces with varying styles and geometries.

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Notes

  1. 1.

    See implementation at https://github.com/ICVL-BGU/Fragment-Style-Recognition.

  2. 2.

    Download the POMPAAF dataset at https://tinyurl.com/ynwc6ymm.

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Acknowledgments

This work has been funded in part by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 964854 (the RePAIR project). We also thank the Helmsley Charitable Trust through the ABC Robotics Initiative and the Frankel Fund of the Computer Science Department at Ben-Gurion University for their generous support.

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Authors and Affiliations

  1. Ben-Gurion University of the Negev, Beersheba, Israel

    Gur Elkin, Ofir Itzhak Shahar, Yaniv Ohayon, Nadav Alali & Ohad Ben-Shahar

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  1. Gur Elkin
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  2. Ofir Itzhak Shahar
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  3. Yaniv Ohayon
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Correspondence to Ohad Ben-Shahar .

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  1. Eindhoven University of Technology, Eindhoven, The Netherlands

    Matthias Rauterberg

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Elkin, G., Shahar, O.I., Ohayon, Y., Alali, N., Ben-Shahar, O. (2025). Recognizing Artistic Style of Archaeological Image Fragments Using Deep Style Extrapolation. In: Rauterberg, M. (eds) Culture and Computing. HCII 2025. Lecture Notes in Computer Science, vol 15800. Springer, Cham. https://doi.org/10.1007/978-3-031-93160-4_8

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  • DOI: https://doi.org/10.1007/978-3-031-93160-4_8

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