Leveraging large scale data for video retrieval

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buir.advisorŞahin, Pınar Duygulu
dc.contributor.authorArmağan, Anıl
dc.date.accessioned2016-01-08T18:28:14Z
dc.date.available2016-01-08T18:28:14Z
dc.date.issued2014
dc.descriptionAnkara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent University, 2014.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2014.en_US
dc.descriptionIncludes bibliographical references leaves 75-82.en_US
dc.description.abstractThe large amount of video data shared on the web resulted in increased interest on retrieving videos using usual cues, since textual cues alone are not sufficient for satisfactory results. We address the problem of leveraging large scale image and video data for capturing important characteristics in videos. We focus on three different problems, namely finding common patterns in unusual videos, large scale multimedia event detection, and semantic indexing of videos. Unusual events are important as being possible indicators of undesired consequences. Discovery of unusual events in videos is generally attacked as a problem of finding usual patterns. With this challenging problem at hand, we propose a novel descriptor to encode the rapid motions in videos utilizing densely extracted trajectories. The proposed descriptor, trajectory snippet histograms, is used to distinguish unusual videos from usual videos, and further exploited to discover snapshots in which unusualness happen. Next, we attack the Multimedia Event Detection (MED) task. We approach this problem as representing the videos in the form of prototypes, that correspond to models each describing a different visual characteristic of a video shot. Finally, we approach the Semantic Indexing (SIN) problem, and collect web images to train models for each concept.en_US
dc.description.provenanceMade available in DSpace on 2016-01-08T18:28:14Z (GMT). No. of bitstreams: 1 0006695.pdf: 4685119 bytes, checksum: e40fb63747ea0f3a06c8f781d0171a12 (MD5)en
dc.description.statementofresponsibilityArmağan, Anılen_US
dc.format.extentxiv, 82 leaves, illistrations, graphicsen_US
dc.identifier.itemidB135346
dc.identifier.urihttp://hdl.handle.net/11693/15992
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectLarge Scale Video Retrievalen_US
dc.subjectMultimedia Event Detectionen_US
dc.subjectUnusual Videosen_US
dc.subjectSemantic Indexingen_US
dc.subject.lccTK6680.5 .A75 2014en_US
dc.subject.lcshDigital video.en_US
dc.subject.lcshMultimedia systems.en_US
dc.titleLeveraging large scale data for video retrievalen_US
dc.typeThesisen_US
thesis.degree.disciplineComputer Engineering
thesis.degree.grantorBilkent University
thesis.degree.levelMaster's
thesis.degree.nameMS (Master of Science)

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