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dc.contributor.advisorŞahin, Pınar Duygulu
dc.contributor.authorUyanık, Özge
dc.date.accessioned2016-01-08T20:06:12Z
dc.date.available2016-01-08T20:06:12Z
dc.date.issued2013
dc.identifier.urihttp://hdl.handle.net/11693/17073
dc.descriptionAnkara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent Univ., 2013.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2013.en_US
dc.descriptionIncludes bibliographical references leaves 55-61.en_US
dc.description.abstractThe Multiple Instance Learning (MIL) paradigm arises to be useful in many application domains, whereas it is particularly suitable for computer vision problems due to the difficulty of obtaining manual labeling. Multiple Instance Learning methods have large applicability to a variety of challenging learning problems in computer vision, including object recognition and detection, tracking, image classification, scene classification and more. As opposed to working with single instances as in standard supervised learning, Multiple Instance Learning operates over bags of instances. A bag is labeled as positive if it is known to contain at least one positive instance; otherwise it is labeled as negative. The overall learning task is to learn a model for some concept using a training set that is formed of bags. A vital component of using Multiple Instance Learning in computer vision is its design for abstracting the visual problem to multi-instance representation, which involves determining what the bag is and what are the instances in the bag. In this context, we consider three different computer vision problems and propose solutions for each of them via novel representations. The first problem is image retrieval and re-ranking; we propose a method that automatically constructs multiple candidate Multi-instance bags, which are likely to contain relevant images. The second problem we look into is recognizing actions from still images, where we extract several candidate object regions and approach the problem of identifying related objects from a weakly supervised point of view. Finally, we address the recognition of human interactions in videos within a MIL framework. In human interaction recognition, videos may be composed of frames of different activities, and the task is to identify the interaction in spite of irrelevant activities that are scattered through the video. To overcome this problem, we use the idea of Multiple Instance Learning to tackle irrelevant actions in the whole video sequence classification. Each of the outlined problems are tested on benchmark datasets of the problems and compared with the state-of-the-art. The experimental results verify the advantages of the proposed MIL approaches to these vision problems.en_US
dc.description.statementofresponsibilityUyanık, Özgeen_US
dc.format.extentx, 61 leaves, graphicsen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectComputer visionen_US
dc.subjectMultiple instance learningen_US
dc.subjectImage retrievalen_US
dc.subjectImage re-rankingen_US
dc.subjectAction recognition in imagesen_US
dc.subjectMultiple featuresen_US
dc.subjectInteraction recognitionen_US
dc.subject.lccTK7872.D48 U93 2013en_US
dc.subject.lcshWireless sensor networks.en_US
dc.subject.lcshSensor networks.en_US
dc.subject.lcshScheduling.en_US
dc.subject.lcshMultichannel communication.en_US
dc.titleMulti-channel TDMA scheduling in wireless sensor networksen_US
dc.typeThesisen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US
dc.identifier.itemidB123492


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