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Geographic Image Retrieval Using Interest Point Descriptors

  • Shawn Newsam
  • Yang Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4842)

Abstract

We investigate image retrieval using interest point descriptors. New geographic information systems such as Google Earth and Microsoft Virtual Earth are providing increased access to remote sensed imagery. Content-based access to this data would support a much richer interaction than is currently possible. Interest point descriptors have proven surprisingly effective for a range of computer vision problems. We investigate their application to performing similarity retrieval in a ground-truth dataset manually constructed from 1-m IKONOS satellite imagery. We compare results of using quantized versus full descriptors, Euclidean versus Mahalanobis distance measures, and methods for comparing the sets of descriptors associated with query and target images.

Keywords

Image Retrieval Mahalanobis Distance Interest Point Query Image Scale Invariant Feature Transform 
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 2007

Authors and Affiliations

  • Shawn Newsam
    • 1
  • Yang Yang
    • 1
  1. 1.Computer Science and Engineering, University of California, Merced CA 95344USA

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