Leveraging large scale data for video retrieval
Author
Armağan, Anıl
Advisor
Şahin, Pınar Duygulu
Date
2014Publisher
Bilkent University
Language
English
Type
ThesisItem 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.