• Documents
  • Authors
  • Tables
  • Log in
  • Sign up
  • MetaCart
  • DMCA
  • Donate

CiteSeerX logo

Advanced Search Include Citations
Advanced Search Include Citations | Disambiguate

DMCA

The PASCAL Visual Object Classes (VOC) challenge (2010)

Cached

  • Download as a PDF

Download Links

  • [www.dai.ed.ac.uk]
  • [homepages.inf.ed.ac.uk]
  • [eprints.pascal-network.org]
  • [www.pascal-network.org]
  • [pascallin.ecs.soton.ac.uk]
  • [www.comp.leeds.ac.uk]
  • [eprints.pascal-network.org]
  • [research.microsoft.com]
  • [www.research.ed.ac.uk]

  • Save to List
  • Add to Collection
  • Correct Errors
  • Monitor Changes
by Mark Everingham , Luc Van Gool , C. K. I. Williams , J. Winn , Andrew Zisserman
Citations:615 - 20 self
  • Summary
  • Citations
  • Active Bibliography
  • Co-citation
  • Clustered Documents
  • Version History

BibTeX

@MISC{Everingham10thepascal,
    author = {Mark Everingham and Luc Van Gool and C. K. I. Williams and J. Winn and Andrew Zisserman},
    title = { The PASCAL Visual Object Classes (VOC) challenge},
    year = {2010}
}

Share

Facebook Twitter Reddit Bibsonomy

OpenURL

 

Abstract

... is a benchmark in visual object category recognition and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has become accepted as the benchmark for object detection. This paper describes the dataset and evaluation procedure. We review the state-of-the-art in evaluated methods for both classification and detection, analyse whether the methods are statistically different, what they are learning from the images (e.g. the object or its context), and what the methods find easy or confuse. The paper concludes with lessons learnt in the three year history of the challenge, and proposes directions for future improvement and extension.

Keyphrases

pascal visual object class    visual object category recognition    evaluation procedure    machine learning community    year history    standard dataset    evaluated method    object detection    standard evaluation procedure    future improvement   

Powered by: Apache Solr
  • About CiteSeerX
  • Submit and Index Documents
  • Privacy Policy
  • Help
  • Data
  • Source
  • Contact Us

Developed at and hosted by The College of Information Sciences and Technology

© 2007-2019 The Pennsylvania State University