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Durham Research Online
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Artefact Classification: A Deep Learning Approach to Fight Against Illicit Trafficking

Winterbottom, Tom and Leone, Anna and Al Moubayed, Noura (2022) 'Artefact Classification: A Deep Learning Approach to Fight Against Illicit Trafficking.', Scientific Reports .

Abstract

We approach the task of detecting the illicit movement of cultural heritage from a machine learning perspective by presenting a framework for detecting a known artefact in a new and unseen image. To this end, we explore the machine learning problem of instance classification for large archaeological images datasets, i.e. where each individual object (instance) is itself a class that all of the multiple images of that object belongs. We focus on a wide variety of objects in the Durham Oriental Museum with which we build a dataset with over 24,502 images of 4,332 unique object instances. We experiment with state-of-the-art convolutional neural network models, the smaller variations of which are suitable for deployment on mobile applications. We find the exact object instance of a given image can be predicted from among 4,332 others with ∼72% accuracy, showing how effectively machine learning can detect a known object from a new image. We demonstrate that accuracy significantly improves as the number of images-per-object instance increases (up to ∼83%), with an ensemble of classifiers scoring as high as 84%. We find that the correct instance is found in the top 3, 5, or 10 predictions of our best models ∼91%, ∼93%, or ∼95% of the time respectively. Our findings contribute to the emerging overlap of machine learning and cultural heritage, and highlights the potential available to future applications and research.

Item Type:Article
Full text:Publisher-imposed embargo
(AM) Accepted Manuscript
File format - PDF
(2937Kb)
Status:Peer-reviewed
Publisher Web site:https://www.nature.com/srep/
Date accepted:01 July 2022
Date deposited:05 July 2022
Date of first online publication:2022
Date first made open access:No date available

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