Comparative evaluation of binary features

Jared Heinly, Enrique Dunn, Jan Michael Frahm

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

284 Scopus citations

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 ).

Original languageEnglish
Title of host publicationComputer Vision, ECCV 2012 - 12th European Conference on Computer Vision, Proceedings
Pages759-773
Number of pages15
EditionPART 2
DOIs
StatePublished - 2012
Event12th European Conference on Computer Vision, ECCV 2012 - Florence, Italy
Duration: 7 Oct 201213 Oct 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7573 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th European Conference on Computer Vision, ECCV 2012
Country/TerritoryItaly
CityFlorence
Period7/10/1213/10/12

Keywords

  • binary features
  • comparison
  • evaluation

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