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dc.contributor.authorÇavuş, Özgeen_US
dc.contributor.authorAksoy, Selimen_US
dc.coverage.spatialOrlando, Florida
dc.date.accessioned2016-02-08T11:35:57Z
dc.date.available2016-02-08T11:35:57Z
dc.date.issued2008-12en_US
dc.identifier.urihttp://hdl.handle.net/11693/26791
dc.descriptionConference name: Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR), 2008
dc.descriptionDate of Conference: 04-06 December, 2008
dc.description.abstractWe describe an annotation and retrieval framework that uses a semantic image representation by contextual modeling of images using occurrence probabilities of concepts and objects. First, images are segmented into regions using clustering of color features and line structures. Next, each image is modeled using the histogram of the types of its regions, and Bayesian classifiers are used to obtain the occurrence probabilities of concepts and objects using these histograms. Given the observation that a single class with the highest probability is not sufficient to model image content in an unconstrained data set with a large number of semantically overlapping classes, we use the concept/object probabilities as a new representation, and perform retrieval in the semantic space for further improvement of the categorization accuracy. Experiments on the TRECVID and Corel data sets show good performance. © 2008 Springer Berlin Heidelberg.en_US
dc.language.isoEnglishen_US
dc.source.titleProceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognitionen_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/978-3-540-89689-0_44en_US
dc.subjectImage analysisen_US
dc.subjectImage enhancementen_US
dc.subjectInformation theoryen_US
dc.subjectPattern recognitionen_US
dc.subjectProbabilityen_US
dc.subjectRandom processesen_US
dc.subjectSemanticsen_US
dc.subjectSurface plasmon resonanceen_US
dc.subjectSyntacticsen_US
dc.subjectTechnical presentationsen_US
dc.subjectBayesian classifiersen_US
dc.subjectColor featuresen_US
dc.subjectContextual modelingen_US
dc.subjectData setsen_US
dc.subjectImage annotationsen_US
dc.subjectLine structuresen_US
dc.subjectModel imagesen_US
dc.subjectOccurrence probabilitiesen_US
dc.subjectRetrieval frameworksen_US
dc.subjectSemantic imagesen_US
dc.subjectSemantic scene classificationsen_US
dc.subjectSemantic spacesen_US
dc.subjectTrecviden_US
dc.subjectObject recognitionen_US
dc.subjectLine segment
dc.subjectProbabilistic latent semantic analysis
dc.titleSemantic scene classification for image annotation and retrievalen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Industrial Engineeringen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage402en_US
dc.citation.epage410en_US
dc.identifier.doi10.1007/978-3-540-89689-0_44en_US
dc.publisherSpringer


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