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The 2005 PASCAL visual object classes challenge

The 2005 PASCAL visual object classes challenge

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Authors

  • Mark Everingham
  • Andrew Zisserman
  • Christopher K. I. Williams
  • Luc Van Gool
  • Moray Allan
  • Christopher M. Bishop
  • Olivier Chapelle
  • Navneet Dalal
  • Thomas Deselaers
  • Gyuri Dorko
  • Stefan Duffner
  • Jan Eichhorn
  • Jason D. R. Farquhar
  • Mario Fritz
  • Christophe Garcia
  • Tom Griffiths
  • Frederic Jurie
  • Daniel Keysers
  • Markus Koskela
  • Jorma Laaksonen
  • Diane Larlus
  • Bastian Leibe
  • Hongying Meng
  • Hermann Ney
  • Bernt Schiele
  • Cordelia Schmid
  • Edgar Seemann
  • John Shawe-Taylor
  • Amos Storkey
  • Sandor Szedmak
  • Bill Triggs
  • Ilkay Ulusoy
  • Ville Viitaniemi

Research units

Info

Original languageEnglish
Title of host publicationMachine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment
Subtitle of host publicationFirst PASCAL Machine Learning Challenges Workshop, MLCW 2005, Southampton, UK, April 11-13, 2005, Revised Selected Papers
EditorsJoaquin Quinonero-Candela, Ido Dagan, Bernardo Magnini, Florence d'Alche-Buc
Place of PublicationBerlin
PublisherSpringer
Pages117-176
Number of pages60
ISBN (Print)9783540334279 , 3540334270
DOIs
StatePublished - 2006
EventFirst PASCAL Machine Learning Challenges Workshop - Southampton, United Kingdom

Publication series

NameLecture notes in computer science
PublisherSpringer
Volume3944

Workshop

WorkshopFirst PASCAL Machine Learning Challenges Workshop
Abbreviated titleMLCW 2005
CountryUnited Kingdom
CitySouthampton
Period11/04/0513/04/05
Internet address

Abstract

The PASCAL Visual Object Classes Challenge ran from February to March 2005. The goal of the challenge was to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). Four object classes were selected: motor-bikes, bicycles, cars and people. Twelve teams entered the challenge. In this chapter we provide details of the datasets, algorithms used by the
teams, evaluation criteria, and results achieved.

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