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Per-patch Descriptor Selection Using Surface and Scene Properties

  • Ivo Everts
  • Jan C. van Gemert
  • Theo Gevers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7577)

Abstract

Local image descriptors are generally designed for describing all possible image patches. Such patches may be subject to complex variations in appearance due to incidental object, scene and recording conditions. Because of this, a single-best descriptor for accurate image representation under all conditions does not exist. Therefore, we propose to automatically select from a pool of descriptors the one that is best suitable based on object surface and scene properties. These properties are measured on the fly from a single image patch through a set of attributes. Attributes are input to a classifier which selects the best descriptor. Our experiments on a large dataset of colored object patches show that the proposed selection method outperforms the best single descriptor and a-priori combinations of the descriptor pool.

Keywords

Average Precision Image Patch Image Descriptor Disturbance Level Descriptor Selection 
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

  • Ivo Everts
    • 1
  • Jan C. van Gemert
    • 1
  • Theo Gevers
    • 1
  1. 1.Intelligent Systems Lab Amsterdam (ISLA)University of AmsterdamAmsterdamThe Netherlands

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