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Odd Leaf Out Combining Human and Computer Vision

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

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Odd Leaf Out Combining Human and Computer Vision

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  1. 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

  2. Refining Metadata Associated with Images

  3. Existing Image Crowdsourcing Games

  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. Game Mechanics

  6. Game Mechanics

  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. 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. Game Variations

  10. Mechanical Turk Trial

  11. Mechanical Turk Trial

  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. Funding This work is made possible by National Science Foundation grant number 0968546

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