A Grid-Based Index and Queries for Large-Scale Geo-tagged Video Collections

  • He Ma
  • Sakire Arslan Ay
  • Roger Zimmermann
  • Seon Ho Kim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7240)


Currently a large number of user-generated videos are produced on a daily basis. It is further increasingly common to combine videos with a variety of meta-data that increase their usefulness. In our prior work we have created a framework for integrated, sensor-rich video acquisition (with one instantiation implemented in the form of smartphone applications) which associates a continuous stream of location and direction information with the acquired videos, hence allowing them to be expressed and manipulated as spatio-temporal objects. In this study we propose a novel multi-level grid-index and a number of related query types that facilitate application access to such augmented, large-scale video repositories. Specifically our grid-index is designed to allow fast access based on a bounded radius and viewing direction – two criteria that are important in many applications that use videos. We present performance results with a comparison to a multi-dimensional R-tree implementation and show that our approach can provide significant speed improvements of at least 30%, considering a mix of queries.


Query Processing Voronoi Diagram Index Structure Range Query Video Segment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • He Ma
    • 1
  • Sakire Arslan Ay
    • 1
  • Roger Zimmermann
    • 1
  • Seon Ho Kim
    • 2
  1. 1.School of ComputingNational University of SingaporeSingaporeSingapore
  2. 2.Integrated Media Systems CenterUniversity of Southern CaliforniaLAUSA

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