Extracting semantic knowledge from web context for multimedia IR: a taxonomy, survey and challenges
Author
dc.contributor.author
Bracamonte, Teresa
Author
dc.contributor.author
Bustos, B.
Author
dc.contributor.author
Poblete Labra, Bárbara
Author
dc.contributor.author
Schreck, Tobias
Admission date
dc.date.accessioned
2018-11-23T19:00:48Z
Available date
dc.date.available
2018-11-23T19:00:48Z
Publication date
dc.date.issued
2018-06
Cita de ítem
dc.identifier.citation
Multimedia Tools and Applications Volumen: 77 Número: 11 Páginas: 13853-13889
es_ES
Identifier
dc.identifier.other
10.1007/s11042-017-4997-y
Identifier
dc.identifier.uri
https://repositorio.uchile.cl/handle/2250/152851
Abstract
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Since its invention, the Web has evolved into the largest multimedia repository that has ever existed. This evolution is a direct result of the explosion of user-generated content, explained by the wide adoption of social network platforms. The vast amount of multimedia content requires effective management and retrieval techniques. Nevertheless, Web multimedia retrieval is a complex task because users commonly express their information needs in semantic terms, but expect multimedia content in return. This dissociation between semantics and content of multimedia is known as the semantic gap. To solve this, researchers are looking beyond content-based or text-based approaches, integrating novel data sources. New data sources can consist of any type of data extracted from the context of multimedia documents, defined as the data that is not part of the raw content of a multimedia file. The Web is an extraordinary source of context data, which can be found in explicit or implicit relation to multimedia objects, such as surrounding text, tags, hyperlinks, and even in relevance-feedback. Recent advances in Web multimedia retrieval have shown that context data has great potential to bridge the semantic gap. In this article, we present the first comprehensive survey of context-based approaches for multimedia information retrieval on the Web. We introduce a data-driven taxonomy, which we then use in our literature review of the most emblematic and important approaches that use context-based data. In addition, we identify important challenges and opportunities, which had not been previously addressed in this area.
es_ES
Patrocinador
dc.description.sponsorship
Millennium Nucleus Center for Semantic Web Research
NC120004
Project Enlace-Fondecyt
ENL011/16
Project Fondef
ID16-10222
PhD Scholarship Program of Conicyt, Chile (CONICYT-PCHA/Doctorado Nacional)
2013-63130260