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      • Dept. of Computer Engineering - Master's degree
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      •   BUIR Home
      • University Library
      • Bilkent Theses
      • Theses - Department of Computer Engineering
      • Dept. of Computer Engineering - Master's degree
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      Leveraging large scale data for video retrieval

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      Author
      Armağan, Anıl
      Advisor
      Şahin, Pınar Duygulu
      Date
      2014
      Publisher
      Bilkent University
      Language
      English
      Type
      Thesis
      Item Usage Stats
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      Abstract
      The 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.
      Keywords
      Large Scale Video Retrieval
      Multimedia Event Detection
      Unusual Videos
      Semantic Indexing
      Permalink
      http://hdl.handle.net/11693/15992
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      • Dept. of Computer Engineering - Master's degree 511
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