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Comparative Evaluation of Binary Features

  • Jared Heinly
  • Enrique Dunn
  • Jan-Michael Frahm
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7573)

Abstract

Performance evaluation of salient features has a long-standing tradition in computer vision. In this paper, we fill the gap of evaluation for the recent wave of binary feature descriptors, which aim to provide robustness while achieving high computational efficiency. We use established metrics to embed our assessment into the body of existing evaluations, allowing us to provide a novel taxonomy unifying both traditional and novel binary features. Moreover, we analyze the performance of different detector and descriptor pairings, which are often used in practice but have been infrequently analyzed. Additionally, we complement existing datasets with novel data testing for illumination change, pure camera rotation, pure scale change, and the variety present in photo-collections. Our performance analysis clearly demonstrates the power of the new class of features. To benefit the community, we also provide a website for the automatic testing of new description methods using our provided metrics and datasets ( www.cs.unc.edu/feature-evaluation ).

Keywords

binary features comparison evaluation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jared Heinly
    • 1
  • Enrique Dunn
    • 1
  • Jan-Michael Frahm
    • 1
  1. 1.The University of North Carolina at Chapel HillUSA

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