Odd Leaf Out
Combining Human and Computer Vision
Arijit Biswas, Computer Science and Darcy Lewis, iSchool
Derek Hansen, Je...
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
• Capital...
Game Mechanics
Game Mechanics
How Leaf Sets Are Constructed
• Designed to bring in useful data
• Not too easy or too hard
• Curvature based histograms u...
What’s in it for us if people play this game?
• Identify errors in the dataset
• Discover if color helps humans identify l...
Game Variations
Before Leaf is Chosen
Multiple Guesses Skip
After Leaf is Chosen
Contest after Game is Finished Contest Pr...
Mechanical Turk Trial
1 2 3 4 5
0
5
10
15
20
25
30
Enjoyment
NumberCorrect
Mechanical Turk Trial
1 2 3 4 5
1
2
3
4
5
Difficulty
Enjoyment
Summary
• Anyone can help in Computer Vision research
work.
• Games can be fun for players and useful for
researchers.
• H...
Funding
This work is made possible by National Science Foundation
grant number 0968546
Odd Leaf Out: Combining Human and Computer Vision
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Odd Leaf Out: Combining Human and Computer Vision

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Odd Leaf Out: Combining Human and Computer Vision

  1. 1. Odd Leaf Out Combining Human and Computer Vision Arijit Biswas, 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
  2. 2. Refining Metadata Associated with Images
  3. 3. Existing Image Crowdsourcing Games
  4. 4. 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
  5. 5. Game Mechanics
  6. 6. Game Mechanics
  7. 7. 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.
  8. 8. 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
  9. 9. Game Variations Before Leaf is Chosen Multiple Guesses Skip After Leaf is Chosen Contest after Game is Finished Contest Previous Round Feedback Mechanism When Feedback Occurs
  10. 10. Mechanical Turk Trial 1 2 3 4 5 0 5 10 15 20 25 30 Enjoyment NumberCorrect
  11. 11. Mechanical Turk Trial 1 2 3 4 5 1 2 3 4 5 Difficulty Enjoyment
  12. 12. 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.
  13. 13. Funding This work is made possible by National Science Foundation grant number 0968546

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