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Dataset issues in object recognition

Dataset issues in object recognition

Research output: Chapter in Book/Report/Conference proceedingChapter

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Authors

  • J. Ponce
  • T. L. Berg
  • M. Everingham
  • D. A. Forsyth
  • M. Hebert
  • S. Lazebnik
  • M. Marszalek
  • C. Schmid
  • B. C. Russell
  • A. Torralba
  • C. K. I. Williams
  • J. Zhang
  • A. Zisserman

Research units

Info

Original languageEnglish
Title of host publicationToward category-level object recognition
EditorsJean Ponce, Martial Hebert, Cordelia Schmid, Andrew Zisserman
Place of PublicationBerlin
PublisherSpringer
Pages29-48
Number of pages20
ISBN (Electronic)9783540687955
ISBN (Print)9783540687948
DOIs
StatePublished - 2006

Publication series

NameLecture notes in computer science
PublisherSpringer
Volume4170
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Abstract

Appropriate datasets are required at all stages of object recognition research, including learning visual models of object and scene categories, detecting and localizing instances of these models in images, and evaluating the performance of recognition algorithms. Current datasets are lacking in several respects, and this paper discusses some of the lessons learned from existing efforts, as well as innovative ways to obtain very large and diverse annotated datasets. It also suggests a few criteria for gathering future datasets.

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