Odd Leaf Out-HCIL Symposium 5.26.11
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Odd Leaf Out-HCIL Symposium 5.26.11

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Presentation given at the 2011 HCIL symposium on May 25, 2011.

Presentation given at the 2011 HCIL symposium on May 25, 2011.

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Odd Leaf Out-HCIL Symposium 5.26.11 Odd Leaf Out-HCIL Symposium 5.26.11 Presentation Transcript

  • Odd Leaf OutCombining Human and Computer Vision
    ArijitBiswas, Computer Science and Darcy Lewis, iSchool
    Derek Hansen, Jenny Preece, Dana Rotman-University of Maryland’s iSchool
    David Jacobs, Eric Stevens-University of Maryland Computer Science
    Jen Hammock, Cynthia Parr-The Smithsonian Institution
  • Refining Metadata Associated with Images
    View slide
  • Existing Image Crowdsourcing Games
    View slide
  • How our game is different
    Anyone can play and can provide us with useful information.
    No expertise necessary
    Capitalizes on strengths of humans and algorithms
    Humans are better than algorithms at identifying similarity of images
  • Game Mechanics
  • Game Mechanics
  • How Leaf Sets Are Constructed
    Designed to bring in useful data
    Not too easy or too hard
    Curvature based histograms used to get features from leaf shapes.
    These features are used to find distance between all possible pairs of leaves.
  • What’s in it for us if people play this game?
    Identify errors in the dataset
    Discover if color helps humans identify leaves
    Feedback on how enjoyable or difficult the game is
  • Game Variations
  • Mechanical Turk Trial
  • Mechanical Turk Trial
  • Summary
    Anyone can help in Computer Vision research work.
    Games can be fun for players and useful for researchers.
    Humans are better than machines in judging the similarity of two images.
  • Funding
    This work is made possible by National Science Foundation grant number 0968546