Content-based video copy detection using multimodal analysis

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buir.advisorUlusoy, Özgür
dc.contributor.authorKüçüktunç, Onur
dc.date.accessioned2016-01-08T18:10:41Z
dc.date.available2016-01-08T18:10:41Z
dc.date.issued2009
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionAnkara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2009.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2009.en_US
dc.descriptionIncludes bibliographical references leaves 67-76.en_US
dc.description.abstractHuge and increasing amount of videos broadcast through networks has raised the need of automatic video copy detection for copyright protection. Recent developments in multimedia technology introduced content-based copy detection (CBCD) as a new research field alternative to the watermarking approach for identification of video sequences. This thesis presents a multimodal framework for matching video sequences using a three-step approach: First, a high-level face detector identifies facial frames/shots in a video clip. Matching faces with extended body regions gives the flexibility to discriminate the same person (e.g., an anchor man or a political leader) in different events or scenes. In the second step, a spatiotemporal sequence matching technique is employed to match video clips/segments that are similar in terms of activity. Finally the non-facial shots are matched using low-level visual features. In addition, we utilize fuzzy logic approach for extracting color histogram to detect shot boundaries of heavily manipulated video clips. Methods for detecting noise, frame-droppings, picture-in-picture transformation windows, and extracting mask for still regions are also proposed and evaluated. The proposed method was tested on the query and reference dataset of CBCD task of TRECVID 2008. Our results were compared with the results of top-8 most successful techniques submitted to this task. Experimental results show that the proposed method performs better than most of the state-of-the-art techniques, in terms of both effectiveness and efficiency.en_US
dc.description.degreeM.S.en_US
dc.description.statementofresponsibilityKüçüktunç, Onuren_US
dc.format.extentxiv, 76 leaves, illustrationsen_US
dc.identifier.itemidBILKUTUPB117765
dc.identifier.urihttp://hdl.handle.net/11693/14899
dc.language.isoEnglishen_US
dc.publisherBilkent Universityen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBILKUTUPB117765en_US
dc.subjectCopy detectionen_US
dc.subjectVideo processingen_US
dc.subjectShot-boundary detectionen_US
dc.subjectVideo segmentation,en_US
dc.subjectSubsequence matchingen_US
dc.subject.lccQA76.575 .K83 2009en_US
dc.subject.lcshMultimedia systems.en_US
dc.subject.lcshDigital video.en_US
dc.subject.lcshVideo recordings.en_US
dc.subject.lcshDigital computer.en_US
dc.titleContent-based video copy detection using multimodal analysisen_US
dc.typeThesisen_US
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Dept. of Computer Engineering - Master's degree