Descriptor Learning for Efficient Retrieval View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2010

AUTHORS

David Hutchison , Takeo Kanade , Josef Kittler , Jon M. Kleinberg , Friedemann Mattern , John C. Mitchell , Moni Naor , Oscar Nierstrasz , C. Pandu Rangan , Bernhard Steffen , Madhu Sudan , Demetri Terzopoulos , Doug Tygar , Moshe Y. Vardi , Gerhard Weikum , James Philbin , Michael Isard , Josef Sivic , Andrew Zisserman

ABSTRACT

For the case of particular object retrieval, we demonstrate impressive gains in performance on a ground truth dataset: our learnt 32-D descriptor without spatial re-ranking outperforms a baseline method using 128-D SIFT descriptors with spatial re-ranking.

PAGES

677-691

References to SciGraph publications

  • 2004-11. Distinctive Image Features from Scale-Invariant Keypoints in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2008. Kernel Codebooks for Scene Categorization in COMPUTER VISION – ECCV 2008
  • 2004-10. Scale & Affine Invariant Interest Point Detectors in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2008. Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search in COMPUTER VISION – ECCV 2008
  • Book

    TITLE

    Computer Vision – ECCV 2010

    ISBN

    978-3-642-15557-4
    978-3-642-15558-1

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-15558-1_49

    DOI

    http://dx.doi.org/10.1007/978-3-642-15558-1_49

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1045882552


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