Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/20295
Title: Machine Learning and Digital Heritage: The CEPROQHA Project Perspective
Authors: Belhi, A
Gasmi, H
Bouras, A
Alfaqheri, T
Aondoakaa, AS
Sadka, AH
Foufou, S
Keywords: Cultural heritage;Machine learning;Artificial intelligence;CEPROQHA project;3D-holoscopic imaging
Issue Date: 3-Jan-2020
Publisher: Springer
Citation: 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
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.
URI: http://bura.brunel.ac.uk/handle/2438/20295
DOI: http://dx.doi.org/10.1007/978-981-32-9343-4_29
ISBN: 9789813293427
ISSN: 2194-5357
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

Files in This Item:
File Description SizeFormat 
FullText.pdfEmbargoed until 03 Jan 2021386.52 kBAdobe PDFView/Open


Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.