• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
Odd Leaf Out-HCIL Symposium 5.26.11
 

Odd Leaf Out-HCIL Symposium 5.26.11

on

  • 300 views

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

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

Statistics

Views

Total Views
300
Views on SlideShare
300
Embed Views
0

Actions

Likes
0
Downloads
0
Comments
0

0 Embeds 0

No embeds

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    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
    • Existing Image Crowdsourcing Games
    • 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