Recognizing Artistic Style of Archaeological Image Fragments Using Deep Style Extrapolation

TL;DR

This paper introduces a deep learning framework that accurately classifies the artistic style of fragmented archaeological images, even with varying styles and geometries, aiding artifact identification.

Contribution

The work presents a novel deep-learning approach for classifying archaeological image fragments by artistic style, handling diverse styles and fragment geometries.

Findings

Achieved state-of-the-art accuracy in style classification

Effectively handled fragments with multiple styles and geometries

Demonstrated robustness across different artifact types

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.