Recognizing Artistic Style of Archaeological Image Fragments Using Deep Style Extrapolation
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866929674798497792 |
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| author | Elkin, Gur Shahar, Ofir Itzhak Ohayon, Yaniv Alali, Nadav Ben-Shahar, Ohad |
| author_facet | Elkin, Gur Shahar, Ofir Itzhak Ohayon, Yaniv Alali, Nadav Ben-Shahar, Ohad |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_00836 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Recognizing Artistic Style of Archaeological Image Fragments Using Deep Style Extrapolation Elkin, Gur Shahar, Ofir Itzhak Ohayon, Yaniv Alali, Nadav Ben-Shahar, Ohad Computer Vision and Pattern Recognition 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. |
| title | Recognizing Artistic Style of Archaeological Image Fragments Using Deep Style Extrapolation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2501.00836 |