Semantic scene classification for content-based image retrieval

dc.contributor.advisorAksoy, Selim
dc.contributor.authorÇavuş, Özge
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionAnkara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2008.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2008.en_US
dc.descriptionIncludes bibliographical references leaves 60-64.en_US
dc.description.abstractContent-based image indexing and retrieval have become important research problems with the use of large databases in a wide range of areas. Because of the constantly increasing complexity of the image content, low-level features are no longer sufficient for image content representation. In this study, a content-based image retrieval framework that is based on scene classification for image indexing is proposed. First, the images are segmented into regions by using their color and line structure information. By using the line structures of the images the regions that do not consist of uniform colors such as man made structures are captured. After all regions are clustered, each image is represented with the histogram of the region types it contains. Both multi-class and one-class classification models are used with these histograms to obtain the probability of observing different semantic classes in each image. Since 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, the obtained probability values are used as a new representation of the images and retrieval is performed on these new representations. In order to minimize the semantic gap, a relevance feedback approach that is based on the support vector data description is also incorporated. Experiments are performed on both Corel and TRECVID datasets and successful results are obtained.en_US
dc.description.statementofresponsibilityÇavuş, Özgeen_US
dc.format.extentxiii, 69 leaves, illustrations, tables, graphsen_US
dc.publisherBilkent Universityen_US
dc.subjectContent based image retrievalen_US
dc.subjectRelevance feedbacken_US
dc.subjectScene classificationen_US
dc.subject.lccTA1632 .C38 2008en_US
dc.subject.lcshImage processing.en_US
dc.subject.lcshSemantic networks (Information theory)en_US
dc.titleSemantic scene classification for content-based image retrievalen_US
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