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dc.contributor.authorNadeem, Muhammad Shahroz
dc.contributor.authorFranqueira, Virginia N. L.
dc.contributor.authorZhai, Xiaojun
dc.contributor.authorKurugollu, Fatih
dc.date.accessioned2019-06-28T12:14:41Z
dc.date.available2019-06-28T12:14:41Z
dc.date.issued2019-06-24
dc.identifier.citationNadeem, M.S., Franqueira, V.N.L., Zhai, X., and Kurugollu, F. (2019) 'A survey of deep learning solutions for multimedia visual content analysis', IEEE Access. doi: 10.1109/ACCESS.2019.2924733.en_US
dc.identifier.issn2169-3536
dc.identifier.doi10.1109/ACCESS.2019.DOI
dc.identifier.urihttp://hdl.handle.net/10545/623941
dc.description.abstractThe increasing use of social media networks on handheld devices, especially smartphones with powerful built-in cameras, and the widespread availability of fast and high bandwidth broadband connections, added to the popularity of cloud storage, is enabling the generation and distribution of massive volumes of digital media, including images and videos. Such media is full of visual information and holds immense value in today’s world. The volume of data involved calls for automated visual content analysis systems able to meet the demands of practice in terms of efficiency and effectiveness. Deep Learning (DL) has recently emerged as a prominent technique for visual content analysis. It is data-driven in nature and provides automatic end-to-end learning solutions without the need to rely explicitly on predefined handcrafted feature extractors. Another appealing characteristic of DL solutions is the performance they can achieve, once the network is trained, under practical constraints. This paper identifies eight problem domains which require analysis of visual artefacts in multimedia. It surveys the recent, authoritative, and best performing DL solutions and lists the datasets used in the development of these deep methods for the identified types of visual analysis problems. The paper also discusses the challenges that DL solutions face which can compromise their reliability, robustness, and accuracy for visual content analysis.en_US
dc.description.sponsorshipN/Aen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.urlhttps://ieeexplore.ieee.org/document/8744516en_US
dc.subjectVisual Content Analysisen_US
dc.subjectDeep Learningen_US
dc.subjectMachine Learningen_US
dc.subjectDataseten_US
dc.titleA survey of deep learning solutions for multimedia visual content analysis.en_US
dc.typeArticleen_US
dc.identifier.eissn2169-3536
dc.contributor.departmentUniversity of Derbyen_US
dc.contributor.departmentUniversity of Essexen_US
dc.identifier.journalIEEE Accessen_US
dcterms.dateAccepted2019-06-10
refterms.dateFOA2019-06-28T12:14:41Z
dc.author.detail785317en_US


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