Image information mining using spatial relationship constraints

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buir.advisorAksoy, Selim
dc.contributor.authorKarakuş, Fatih
dc.date.accessioned2016-01-08T18:23:19Z
dc.date.available2016-01-08T18:23:19Z
dc.date.issued2012
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionAnkara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent University, 2012.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2012.en_US
dc.descriptionIncludes bibliographical refences.en_US
dc.description.abstractThere is a huge amount of data which is collected from the Earth observation satellites and they are continuously sending data to Earth receiving stations day by day. Therefore, mining of those data becomes more important for effective processing of collected multi-spectral images. The most popular approaches for this problem use the meta-data of the images such as geographical coordinates etc. However, these approaches do not offer a good solution for determining what those images contain. Some researches make a big step from the meta-data based approaches in this area by moving the focus of the study to content based approaches such as utilizing the region information of the sensed images. In this thesis, we propose a novel, generic and extendable image information mining system that uses spatial relationship constraints. In this system, we use not only the region content, but also relationships of those regions. First, we extract the region information of the images and then extract pairwise relationship information of those regions such as left, right, above, below, near, far and distance etc. This feature extraction process is defined as a generic process which is independent from how the region segmentation is obtained. In addition to these, since new features and new approaches are continuously being developed by the image information mining researchers, extendability feature of the our system plays a big role while we are designing our system. In this thesis, we also propose a novel feature vector structure in which a feature vector consists of several sub-feature vectors. In the proposed feature vector structure, each sub-feature vector can be exclusively selected to be used for search process and they can have different distance metrics to be used in comparisons between the same sub-feature vector of the other feature vector structures. Therefore, the system gives ability to users to choose which information about the region and its pairwise relationship with other regions to be used when they perform a search on the system. The proposed system is illustrated by using region based retrieval scenarios on very high spatial resolution satellite images.en_US
dc.description.degreeM.S.en_US
dc.description.statementofresponsibilityKarakuş, Fatihen_US
dc.format.extentxvi, 105 leaves, illustrationsen_US
dc.identifier.itemidB133977
dc.identifier.urihttp://hdl.handle.net/11693/15695
dc.language.isoEnglishen_US
dc.publisherBilkent Universityen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectImage information miningen_US
dc.subjectSpatial relationshipsen_US
dc.subjectContent based image retrievalen_US
dc.subjectImage databasesen_US
dc.subjectImage retrievalen_US
dc.subjectInformation retrievalen_US
dc.subjectRemote sensingen_US
dc.subject.lccTA1637 .K37 2012en_US
dc.subject.lcshImage processing--Databases.en_US
dc.subject.lcshImage processing--Digital techniques.en_US
dc.subject.lcshData mining.en_US
dc.subject.lcshSimulation methods.en_US
dc.subject.lcshComputer simulation.en_US
dc.subject.lcshDatabase searching.en_US
dc.subject.lcshInformation retrieval.en_US
dc.titleImage information mining using spatial relationship constraintsen_US
dc.typeThesisen_US

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