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Machine Learning for Cultural Heritage: A Survey

Fiorucci, M; Khoroshiltseva, M; Pontil, M; Traviglia, A; Del Bue, A; James, S; (2020) Machine Learning for Cultural Heritage: A Survey. Pattern Recognition Letters , 133 pp. 102-108. 10.1016/j.patrec.2020.02.017. Green open access

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Abstract

The application of Machine Learning (ML) to Cultural Heritage (CH) has evolved since basic statistical approaches such as Linear Regression to complex Deep Learning models. The question remains how much of this actively improves on the underlying algorithm versus using it within a ‘black box’ setting. We survey across ML and CH literature to identify the theoretical changes which contribute to the algorithm and in turn them suitable for CH applications. Alternatively, and most commonly, when there are no changes, we review the CH applications, features and pre/post-processing which make the algorithm suitable for its use. We analyse the dominant divides within ML, Supervised, Semi-supervised and Unsupervised, and reflect on a variety of algorithms that have been extensively used. From such an analysis, we give a critical look at the use of ML in CH and consider why CH has only limited adoption of ML.

Type: Article
Title: Machine Learning for Cultural Heritage: A Survey
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.patrec.2020.02.017
Publisher version: https://doi.org/10.1016/j.patrec.2020.02.017
Language: English
Additional information: This article is licensed under a Creative Commons license (https://creativecommons.org/licenses/by/4.0/).
Keywords: Artificial Intelligence, Machine Learning, Cultural Heritage, Digital Humanities
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10093587
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