Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/54718
DC FieldValueLanguage
dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorWang, Z-
dc.creatorFeng, D-
dc.date.accessioned2016-08-18T04:35:36Z-
dc.date.available2016-08-18T04:35:36Z-
dc.identifier.isbn9781616928599 (hbk.)-
dc.identifier.isbn9781616928612 (ebk.)-
dc.identifier.urihttp://hdl.handle.net/10397/54718-
dc.language.isoenen_US
dc.publisherIGI Globalen_US
dc.titleDiscovering semantics from visual informationen_US
dc.typeBook Chapteren_US
dc.identifier.spage116-
dc.identifier.epage145-
dc.identifier.doi10.4018/978-1-61692-859-9.ch006-
dcterms.abstractVisual information has been immensely used in various domains such as web, education, health, and digital libraries, due to the advancements of computing technologies. Meanwhile, users realize that it has been more and more difficult to find desired visual content such as images. Though traditional content-based retrieval (CBR) systems allow users to access visual information through query-by-example with low level visual features (e.g. color, shape, and texture), the semantic gap is widely recognized as a hurdle for practical adoption of CBR systems. Wealthy visual information (e.g. user generated visual content) enables us to derive new knowledge at a large scale, which will significantly facilitate visual information management. Besides semantic concept detection, semantic relationship among concepts can also be explored in visual domain, other than traditional textual domain. Therefore, this chapter aims to provide an overview of the state-of-the-arts on discovering semantics in visual domain from two aspects, semantic concept detection and knowledge discovery from visual information at semantic level. For the first aspect, various aspects of visual information annotation are discussed, including content representation, machine learning based annotation methodologies, and widely used datasets. For the second aspect, a novel data driven based approach is introduced to discover semantic relevance among concepts in visual domain. Future research topics are also outlined.-
dcterms.bibliographicCitationIn CH Wei & Y Li (Eds.), Machine learning techniques for adaptive multimedia retrieval : technologies, applications, and perspectives, p. 116-145. Hershey, Pa.: IGI Global, 2011-
dcterms.issued2011-
dc.relation.ispartofbookMachine learning techniques for adaptive multimedia retrieval : technologies, applications, and perspectives-
dc.publisher.placeHershey, Pa.en_US
dc.identifier.rosgroupidr53785-
dc.description.ros2010-2011 > Academic research: refereed > Chapter in an edited book (author)-
Appears in Collections:Book Chapter
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