Abstract
Through this paper, we aim at investigating the impact of artificial intelligence technologies on cultural heritage promotion and long-term preservation in terms of digitization effectiveness, attractiveness of the assets, and value empowering. Digital tools have been validated to yield sustainable and yet effective preservation for multiple types of content. For cultural data, however, there are multiple challenges in order to achieve sustainable preservation using these digital tools due to the specificities and the high-quality requirements imposed by cultural institutions. With the rise of machine learning and data science technologies, many researchers and heritage organizations are nowadays searching for techniques and methods to value and increase the reliability of cultural heritage digitization through machine learning. The present study investigates some of these initiatives highlighting their added value and potential future improvements. We mostly cover the aspects related to our context which is the long-term cost-effective digital preservation of the Qatari cultural heritage through the CEPROQHA project.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Belhi, A., Foufou, S., Bouras, A., Sadka, A.H.: Digitization and preservation of cultural heritage products. In: IFIP International Conference on Product Lifecycle Management, pp. 241–253. Springer (2017)
Van Noord, N., Hendriks, E., Postma, E.: Towards discovery of the artist’s style: learning to recognize artists by their artworks. IEEE Sig. Process. Mag. 32, 46–54 (2015)
Belhi, A., Bouras, A., Foufou, S.: Leveraging known data for missing label prediction in cultural heritage context. Appl. Sci. 8, 1768 (2018)
Lippmann, G.: Epreuves reversibles donnant la sensation du relief. J. Phys. Theor. Appl. 7, 821–825 (1908)
Swash, M.: Holoscopic 3D Imaging and Display Technology: Camera/Processing/Display. Brunel University, London (2013)
Aggoun, A., Tsekleves, E., Swash, M.R., Zarpalas, D., Dimou, A., Daras, P., Nunes, P., Soares, L.D.: Immersive 3D holoscopic video system. IEEE Multimedia 20, 28–37 (2013)
Alfaqheri, T., Nasri, S.A.E.M., Sadka, A.H.: 3D Holoscopic Imaging for Cultural Heritage Digitalisation. arXiv preprint arXiv:1810.03916 (2018)
Belhi, A., Bouras, A., Foufou, S.: Towards a hierarchical multitask classification framework for cultural heritage. In: 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA), pp. 1–7. IEEE 2018
Belhi, A., Bouras, A.: Towards a multimodal classification of cultural heritage. In: Qatar Foundation Annual Research Conference Proceedings, pp. ICTPD1010. HBKU Press, Qatar (2018)
Doerr, M.: The CIDOC conceptual reference module: an ontological approach to semantic interoperability of metadata. AI Mag. 24, 75 (2003)
Gasmi, H., Bouras, A., Laval, J.: LSTM recurrent neural networks for cybersecurity named entity recognition. ICSEA 2018, 11 (2018)
Buitelaar, P., Cimiano, P., Magnini, B.: Ontology learning from text: an overview. Ontology learning from text: methods, evaluation and applications. Front. Artif. Intel. Appl. Ser. 123 (2005)
Belhi, A., Bouras, A., Foufou, S.: Digitization and preservation of cultural heritage: the CEPROQHA approach. In: Software, Knowledge, Information Management and Applications (SKIMA), 2017 11th International Conference on, pp. 1–7. IEEE 2017
Nadeau, D., Sekine, S.: A survey of named entity recognition and classification. Lingvisticae Invest. 30, 3–26 (2007)
Acknowledgements
This publication was made possible by NPRP grant 9-181-1-036 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors (www.ceproqha.qa).
The authors would also like to thank Mr. Marc Pelletreau, the MIA Multimedia team, the Art Curators and the management staff of the Museum of Islamic art, Doha Qatar for their help and contribution in the data acquisition.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Belhi, A. et al. (2020). Machine Learning and Digital Heritage: The CEPROQHA Project Perspective. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Fourth International Congress on Information and Communication Technology. Advances in Intelligent Systems and Computing, vol 1027. Springer, Singapore. https://doi.org/10.1007/978-981-32-9343-4_29
Download citation
DOI: https://doi.org/10.1007/978-981-32-9343-4_29
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-32-9342-7
Online ISBN: 978-981-32-9343-4
eBook Packages: EngineeringEngineering (R0)