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AuthorJboor N.H.
AuthorBelhi A.
AuthorAl-Ali A.K.
AuthorBouras A.
AuthorJaoua A.
Available date2020-03-18T10:47:18Z
Publication Date2019
Publication Name2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology, JEEIT 2019 - Proceedings
ResourceScopus
URIhttp://dx.doi.org/10.1109/JEEIT.2019.8717470
URIhttp://hdl.handle.net/10576/13456
AbstractCultural heritage takes an important part in defining the identity and the history of a civilization or a nation. Valuing and preserving this heritage is thus a top priority for governments and heritage institutions. Through this paper, we present an image completion (inpainting) approach adapted for the curation and the completion of damaged artwork. Our approach uses a set of machine learning techniques such as Generative Adversarial Networks which are among the most powerful generative models that can be trained to generate realistic data samples. As we are focusing mostly on visual cultural heritage, the pipeline of our framework has many optimizations such as the use of clustering to optimize the training of the generative part to ensure a better performance across a variety of cultural data categories. The experimental results of our framework are promising and were validated on a dataset of paintings.
SponsorThis 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.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectCultural Heritage
Deep Learning
Generative Adversarial Networks
Image Inpainting
TitleTowards an Inpainting Framework for Visual Cultural Heritage
TypeConference Paper
Pagination602-607
dc.accessType Abstract Only


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