Çavuş, ÖzgeAksoy, Selim2016-02-082016-02-082008-12http://hdl.handle.net/11693/26791Conference name: Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR), 2008Date of Conference: 04-06 December, 2008We 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.EnglishImage analysisImage enhancementInformation theoryPattern recognitionProbabilityRandom processesSemanticsSurface plasmon resonanceSyntacticsTechnical presentationsBayesian classifiersColor featuresContextual modelingData setsImage annotationsLine structuresModel imagesOccurrence probabilitiesRetrieval frameworksSemantic imagesSemantic scene classificationsSemantic spacesTrecvidObject recognitionLine segmentProbabilistic latent semantic analysisConference Paper10.1007/978-3-540-89689-0_44