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Labeling images with a computer game

Labeling images with a computer game. By von Ahn & Dabbish. Motivation. ‘Our goal is ambitious: to label the majority of images on the World Wide Web.’ Machine learning to identify and label image components is generally ineffective. Past Work. Lempel & Soffer , 2002

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Labeling images with a computer game

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  1. Labeling images with a computer game By von Ahn & Dabbish

  2. Motivation • ‘Our goal is ambitious: to label the majority of images on the World Wide Web.’ • Machine learning to identify and label image components is generally ineffective. Peter Landwehr

  3. Past Work • Lempel & Soffer, 2002 • Duygulu et al., 2002 Peter Landwehr

  4. Peter Landwehr

  5. Implementation notes • Image reintroduction possibility • Age restriction possibility • Play alone possibility • Taboo threshold (X=1) • Image completion • Randomized partners • 350K images from random.bounceme.net • 73K word dictionary • ‘[T]he game doesn’t ask the players to describe the image: all they are told is that they have to “think like each other” and type the same string.’ Peter Landwehr

  6. August 9 – December 10, 2003 • 13,360 Players • 1,271,451 labels for 293,760 images • 80% of players returned at least once, 33 spent 50 hours playing • Mean = 3.89 labels/image/minute of play, std. dev = 0.69. Peter Landwehr

  7. Validation (1) Peter Landwehr

  8. Validation (2) • ‘Please type the six individual words that you feel best describe the contents of this image. Type one word per line below; words should be less than 13 character’ • For all of the images… • at least 5 (83%) of the 6 labels produced by the game were entered by at least one participant. • the three most common words entered by participants were contained among the labels produced by the game. • 15 participants aged 20-25,20 images with >5 labels Peter Landwehr

  9. Validation (3) • ‘How many of the words above would you use in describing this image to someone who couldn’t see it?’ • Mean = 5.105, std. dev = 1.3087 (85% useful) • ‘How many of the words have nothing to do with theimage (i.e., you don't understand why they are listed with this image)?’ • Mean = 0.105, std. dev = 0.2529 (1.7% useless) • 15 participants aged 20-25,20 images with >5 labels Peter Landwehr

  10. Broad implications • ‘At this rate, 5,000 people playing the ESP game 24 hours a day would label all images on Google (425,000,000 images) [with one tag] in 31 days.’ • ‘…[O]ur main contribution stems from the way in which we attack the labeling problem. …[W]e have shown that it’s conceivable that a large-scale problem can be solved with a method that uses people playing on the Web. We’ve turned tedious work into something people want to do.’ Peter Landwehr

  11. ‘The ESP game can be used, with minor modifications, to label sound or video clips (i.e., there is nothing inherent about images).’ Peter Landwehr

  12. ‘Other problems that could be solved by having people play games include categorizing web pages into topics and monitoring security cameras.’ Peter Landwehr

  13. More recent work Walsh & Golbeck, 2010 Peter Landwehr

  14. Recent Work • McGonigal, 2003 • Law, von Ahn, Dannenberg, and Crawford, 2007 • Hacker and von Ahn, 2009 • Dong and Fu, 2010 • Walsh and Golbeck, 2010 Peter Landwehr

  15. Questions?

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