Odd Leaf Out Combining Human and Computer Vision Arijit Biswas, Computer Science and Darcy Lewis, iSchool Derek Hansen, Jenny Preece, Dana Rotman-University.

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Presentation transcript:

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

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

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

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