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Mining Large Image Collections

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Data Mining for Scientific and Engineering Applications

Part of the book series: Massive Computing ((MACO,volume 2))

Abstract

NASA has been involved with remote exploration of the solar system for over forty years and, as a result, has accumulated a vast archive of images. Continued improvements in acquisition and storage technology are yielding new image sets with data volumes measured in terabytes. Within these large image collections there is a wealth of scientific information, but getting from the data to knowledge is a difficult problem both due to the size of the datasets involved and the difficulty of automatically interpreting image data. This chapter provides an overview of our efforts to develop algorithms for mining useful information from large image collections.

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Authors
  1. Michael C. Burl
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Editor information

Editors and Affiliations

  1. University of Illinois, Chicago, USA

    Robert L. Grossman

  2. Lawrence Livermore National Laboratory, Livermore, USA

    Chandrika Kamath

  3. Sandia National Laboratories, Livermore, USA

    Philip Kegelmeyer

  4. Army High Performance Computing Research Center (AHPCRC), Minneapolis, USA

    Vipin Kumar

  5. Army Research Laboratory, Aberdeen Proving Ground, USA

    Raju R. Namburu

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© 2001 Springer Science+Business Media Dordrecht

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Burl, M.C. (2001). Mining Large Image Collections. In: Grossman, R.L., Kamath, C., Kegelmeyer, P., Kumar, V., Namburu, R.R. (eds) Data Mining for Scientific and Engineering Applications. Massive Computing, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-1733-7_4

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  • DOI: https://doi.org/10.1007/978-1-4615-1733-7_4

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4020-0114-7

  • Online ISBN: 978-1-4615-1733-7

  • eBook Packages: Springer Book Archive

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