# Recognizing Artistic Style of Archaeological Image Fragments Using Deep Style Extrapolation

arXiv: https://arxiv.org/abs/2501.00836 · HTML: https://mlsanity.com/paper/2501.00836/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jan 2025.

## Built on by (most cited first, 4 of 4)

- [Pairwise Alignment & Compatibility for Arbitrarily Irregular Image Fragments](https://mlsanity.com/paper/2507.09767/) (2025)
- [PuzLM: Solving Jigsaw Puzzles with Sequence-to-Sequence Language Models](https://mlsanity.com/paper/2511.06315/) (2025)
- [Solving Convex Partition Visual Jigsaw Puzzles](https://mlsanity.com/paper/2511.04450/) (2025)
- [Fragment-Aware Vision Transformers for Fresco-Fragment Style Classification](https://mlsanity.com/paper/2609.21012/) (2026)

## Abstract (arXiv)

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.