{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T03:48:32Z","timestamp":1783050512008,"version":"3.54.6"},"reference-count":38,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2020,6,15]],"date-time":"2020-06-15T00:00:00Z","timestamp":1592179200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JEIM"],"published-print":{"date-parts":[[2023,4,24]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>Digital tools have been used to document cultural heritage with high-quality imaging and metadata. However, some of the historical assets are totally or partially unlabeled and some are physically damaged, which decreases their attractiveness and induces loss of value. This paper introduces a new framework that aims at tackling the cultural data enrichment challenge using machine learning.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>This framework focuses on the automatic annotation and metadata completion through new deep learning classification and annotation methods. It also addresses issues related to physically damaged heritage objects through a new image reconstruction approach based on supervised and unsupervised learning.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The authors evaluate approaches on a data set of cultural objects collected from various cultural institutions around the world. For annotation and classification part of this study, the authors proposed and implemented a hierarchical multimodal classifier that improves the quality of annotation and increases the accuracy of the model, thanks to the introduction of multitask multimodal learning. Regarding cultural data visual reconstruction, the proposed clustering-based method, which combines supervised and unsupervised learning is found to yield better quality completion than existing inpainting frameworks.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>This research work is original in sense that it proposes new approaches for the cultural data enrichment, and to the authors\u2019 knowledge, none of the existing enrichment approaches focus on providing an integrated framework based on machine learning to solve current challenges in cultural heritage. These challenges, which are identified by the authors are related to metadata annotation and visual reconstruction.<\/jats:p><\/jats:sec>","DOI":"10.1108\/jeim-02-2020-0059","type":"journal-article","created":{"date-parts":[[2020,6,6]],"date-time":"2020-06-06T07:34:55Z","timestamp":1591428895000},"page":"734-746","source":"Crossref","is-referenced-by-count":53,"title":["A machine learning framework for enhancing digital experiences in cultural heritage"],"prefix":"10.1108","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0580-502X","authenticated-orcid":false,"given":"Abdelhak","family":"Belhi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdelaziz","family":"Bouras","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdulaziz Khalid","family":"Al-Ali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebti","family":"Foufou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"140","published-online":{"date-parts":[[2020,6,15]]},"reference":[{"key":"key2023042108281905200_ref001","first-page":"588","article-title":"Genre and style based painting classification","year":"2015"},{"key":"key2023042108281905200_ref002","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.procs.2019.01.014","article-title":"Multimodal images classification using dense SURF, spectral information and support vector machine","volume":"148","year":"2019","journal-title":"Procedia Computer Science"},{"key":"key2023042108281905200_ref003","first-page":"3541","article-title":"Towards automated classification of fine-art painting style: a comparative study","year":"2012"},{"key":"key2023042108281905200_ref004","first-page":"168","article-title":"Painting classification using a pre-trained convolutional neural network","year":"2016"},{"key":"key2023042108281905200_ref005","first-page":"1023","article-title":"3D reconstruction for a cultural heritage virtual tour system","volume":"37","year":"2008","journal-title":"International Archives of Photogrammetry"},{"key":"key2023042108281905200_ref006","first-page":"404","article-title":"Surf: speeded up robust features","year":"2006"},{"issue":"10","key":"key2023042108281905200_ref007","doi-asserted-by":"crossref","first-page":"1768","DOI":"10.3390\/app8101768","article-title":"Leveraging known data for missing label prediction in cultural heritage context","volume":"8","year":"2018","journal-title":"Applied Sciences"},{"key":"key2023042108281905200_ref008","first-page":"1","article-title":"Towards a hierarchical multitask classification framework for cultural heritage","year":"2018"},{"key":"key2023042108281905200_ref009","first-page":"188","article-title":"Investigating 3D holoscopic visual content upsampling using super-resolution for cultural heritage digitization","volume":"75","year":"2019","journal-title":"Signal Processing: Image Communication"},{"key":"key2023042108281905200_ref010","first-page":"402","article-title":"Deep correlation features for image style classification","year":"2016"},{"key":"key2023042108281905200_ref011","first-page":"248","article-title":"Imagenet: a large-scale hierarchical image database","year":"2009"},{"issue":"3","key":"key2023042108281905200_ref012","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.culher.2009.11.011","article-title":"Cultural heritage interactive 3D models on the web: an approach using open source and free software","volume":"11","year":"2010","journal-title":"Journal of Cultural Heritage"},{"key":"key2023042108281905200_ref013","first-page":"602","article-title":"Towards an inpainting framework for visual cultural heritage","year":"2019"},{"key":"key2023042108281905200_ref014","article-title":"Recognizing image style","year":"2013"},{"key":"key2023042108281905200_ref015","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","year":"2012","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"6","key":"key2023042108281905200_ref016","doi-asserted-by":"crossref","first-page":"1159","DOI":"10.1109\/TPAMI.2011.203","article-title":"Rhythmic brushstrokes distinguish van Gogh from his contemporaries: findings via automated brushstroke extraction","volume":"34","year":"2012","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"10","key":"key2023042108281905200_ref017","doi-asserted-by":"crossref","first-page":"992","DOI":"10.3390\/app7100992","article-title":"Classification of architectural heritage images using deep learning techniques","volume":"7","year":"2017","journal-title":"Applied Sciences"},{"key":"key2023042108281905200_ref018","first-page":"1150","article-title":"Object recognition from local scale-invariant features","year":"1999"},{"key":"key2023042108281905200_ref019","first-page":"1174","article-title":"From part to whole: who is behind the painting?"},{"key":"key2023042108281905200_ref020","first-page":"1183","article-title":"DeepArt: learning joint representations of visual arts","year":"2017"},{"key":"key2023042108281905200_ref021","first-page":"451","article-title":"The rijksmuseum challenge: museum-centered visual recognition","year":"2014"},{"key":"key2023042108281905200_ref022","unstructured":"MET, T. (2019), \u201cThe metropolitan museum of art\u201d, available at: https:\/\/www.metmuseum.org\/."},{"issue":"3","key":"key2023042108281905200_ref023","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1007\/s10588-018-09285-y","article-title":"Modeling and representation of built cultural heritage data using semantic web technologies and building information model","volume":"25","year":"2019","journal-title":"Computational and Mathematical Organization Theory"},{"key":"key2023042108281905200_ref024","first-page":"16","article-title":"Connoisseur: classification of styles of Mexican architectural heritage with deep learning and visual attention prediction","year":"2017"},{"key":"key2023042108281905200_ref025","first-page":"2536","article-title":"Context encoders: feature learning by inpainting","year":"2016"},{"key":"key2023042108281905200_ref026","first-page":"1","article-title":"Color multi-fusion Fisher vector feature for fine art painting categorization and influence analysis","year":"2016"},{"issue":"1","key":"key2023042108281905200_ref027","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.culher.2010.10.004","article-title":"Content management system incorporated in a virtual museum hosting","volume":"12","year":"2011","journal-title":"Journal of Cultural Heritage"},{"key":"key2023042108281905200_ref028","first-page":"1254","article-title":"A unified framework for painting classification","year":"2015"},{"key":"key2023042108281905200_ref029","first-page":"753","article-title":"Visual link retrieval in a database of paintings","year":"2016"},{"issue":"2","key":"key2023042108281905200_ref030","first-page":"8","article-title":"Impressionism, expressionism, surrealism: automated recognition of painters and schools of art","volume":"7","year":"2010","journal-title":"ACM Transactions on Applied Perception (TAP)"},{"key":"key2023042108281905200_ref031","volume-title":"Digital Imaging for Cultural Heritage Preservation: Analysis, Restoration, and Reconstruction of Ancient Artworks","year":"2011"},{"key":"key2023042108281905200_ref032","article-title":"OmniArt: multi-task deep learning for artistic data analysis","year":"2017"},{"key":"key2023042108281905200_ref033","first-page":"3703","article-title":"Ceci n'est pas une pipe: a deep convolutional network for fine-art paintings classification","year":"2016"},{"key":"key2023042108281905200_ref034","first-page":"1561","article-title":"Style retrieval from natural images","year":"2016"},{"issue":"4","key":"key2023042108281905200_ref035","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/MSP.2015.2406955","article-title":"Toward Discovery of the Artist's Style: learning to recognize artists by their artworks","volume":"32","year":"2015","journal-title":"IEEE Signal Processing Magazine"},{"key":"key2023042108281905200_ref036","unstructured":"WikiArt.org. (2019), \u201cWikiArt.Org - visual art Encyclopedia\u201d, available at: https:\/\/www.wikiart.org\/."},{"key":"key2023042108281905200_ref037","first-page":"5485","article-title":"Semantic image inpainting with deep generative models","year":"2017"},{"key":"key2023042108281905200_ref038","article-title":"Generative image inpainting with contextual attention","year":"2018"}],"container-title":["Journal of Enterprise Information Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/JEIM-02-2020-0059\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/JEIM-02-2020-0059\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T22:30:48Z","timestamp":1753396248000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/jeim\/article\/36\/3\/734-746\/203274"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,15]]},"references-count":38,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,6,15]]},"published-print":{"date-parts":[[2023,4,24]]}},"alternative-id":["10.1108\/JEIM-02-2020-0059"],"URL":"https:\/\/doi.org\/10.1108\/jeim-02-2020-0059","relation":{},"ISSN":["1741-0398"],"issn-type":[{"value":"1741-0398","type":"print"}],"subject":[],"published":{"date-parts":[[2020,6,15]]}}}