Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/111806
DC Field | Value | Language |
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dc.contributor.author | Abgaz, Yalemisew | - |
dc.contributor.author | Rocha Souza, Renato | - |
dc.contributor.author | Methuku, Japesh | - |
dc.contributor.author | Koch, Gerda | - |
dc.contributor.author | Dorn, Amelie | - |
dc.date.accessioned | 2024-01-10T19:30:11Z | - |
dc.date.available | 2024-01-10T19:30:11Z | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 2313-433X | pt |
dc.identifier.uri | https://hdl.handle.net/10316/111806 | - |
dc.description.abstract | Cultural heritage images are among the primary media for communicating and preserving the cultural values of a society. The images represent concrete and abstract content and symbolise the social, economic, political, and cultural values of the society. However, an enormous amount of such values embedded in the images is left unexploited partly due to the absence of methodological and technical solutions to capture, represent, and exploit the latent information. With the emergence of new technologies and availability of cultural heritage images in digital formats, the methodology followed to semantically enrich and utilise such resources become a vital factor in supporting users need. This paper presents a methodology proposed to unearth the cultural information communicated via cultural digital images by applying Artificial Intelligence (AI) technologies (such as Computer Vision (CV) and semantic web technologies). To this end, the paper presents a methodology that enables efficient analysis and enrichment of a large collection of cultural images covering all the major phases and tasks. The proposed method is applied and tested using a case study on cultural image collections from the Europeana platform. The paper further presents the analysis of the case study, the challenges, the lessons learned, and promising future research areas on the topic. | pt |
dc.description.sponsorship | ADAPT and the Austrian Academy of Sciences go!digital Next Generation grant (GDNG 2018-051). The ADAPT SFI Centre for Digital Media Technology is funded by Science Foundation Ireland through the SFI Research Centres Programme and isco-funded under the European Regional Development Fund(ERDF) through Grant # 13/RC/2106. | pt |
dc.language.iso | eng | pt |
dc.rights | openAccess | pt |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt |
dc.subject | cultural images | pt |
dc.subject | cultural heritage | pt |
dc.subject | artificial intelligence | pt |
dc.subject | computer vision | pt |
dc.subject | semantic enrichment | pt |
dc.subject | image analysis | pt |
dc.subject | digital humanities | pt |
dc.subject | ontologies | pt |
dc.subject | deep learning | pt |
dc.title | A Methodology for Semantic Enrichment of Cultural Heritage Images Using Artificial Intelligence Technologies | pt |
dc.type | article | - |
degois.publication.firstPage | 121 | pt |
degois.publication.issue | 8 | pt |
degois.publication.title | Journal of Imaging | pt |
dc.peerreviewed | yes | pt |
dc.identifier.doi | 10.3390/jimaging7080121 | pt |
degois.publication.volume | 7 | pt |
dc.date.embargo | 2021-01-01 | * |
uc.date.periodoEmbargo | 0 | pt |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.grantfulltext | open | - |
item.openairetype | article | - |
item.languageiso639-1 | en | - |
item.fulltext | Com Texto completo | - |
item.cerifentitytype | Publications | - |
Appears in Collections: | I&D CEIS20 - Artigos em Revistas Internacionais |
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File | Description | Size | Format | |
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jimaging-07-00121.pdf | 7.57 MB | Adobe PDF | View/Open |
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This item is licensed under a Creative Commons License