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Peekaboom: a game for locating objects in images
Abstract
We introduce Peekaboom, an entertaining web-based game that can help computers locate objects in images. People play the game because of its entertainment value, and as a side effect of them playing, we collect valuable image metadata, such as which pixels belong to which object in the image. The collected data could be applied towards constructing more accurate computer vision algorithms, which require massive amounts of training and testing data not currently available. Peekaboom has been played by thousands of people, some of whom have spent over 12 hours a day playing, and thus far has generated millions of data points. In addition to its purely utilitarian aspect, Peekaboom is an example of a new, emerging class of games, which not only bring people together for leisure purposes, but also exist to improve artificial intelligence. Such games appeal to a general audience, while providing answers to problems that computers cannot yet solve.
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Cited by
LabelMe: A Database and Web-Based Tool for Image Annotation
Russell, B. C., Torralba, A., Murphy, K. P., & Freeman, W. T. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-007-0090-8 · Cited by 4,210
Predicting protein structures with a multiplayer online game
Cooper, S., Khatib, F., Treuille, A., Barbero, J., Lee, J., Beenen, M., Leaver-Fay, A., Baker, D., Popović, Z., & players, F. (2010) · Nature · doi:10.1038/nature09304 · Cited by 1,368
ReferItGame: Referring to Objects in Photographs of Natural Scenes
Kazemzadeh, S., Ordonez, V., Matten, M., & Berg, T. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1086 · Cited by 1,048
Utility data annotation with Amazon Mechanical Turk
Sorokin, A., & Forsyth, D. (2008) · 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2008.4562953 · Cited by 616
A review of verbal and non-verbal human–robot interactive communication
Mavridis, N. (2015) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2014.09.031 · Cited by 453
Metadata
- Authors
- von Ahn, L., Liu, R., & Blum, M.
- Venue
- Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2006-04-22
- Pages
- 55-64
- OpenAlex
- W2080942732
- Cited by
- 509
- References
- 0
- FWCI
- 39.89
- Type
- conference paper
- Language
- en
- Open access
- closed
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- von Ahn, L., Liu, R., & Blum, M. (2006). Peekaboom: a game for locating objects in images. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/1124772.1124782
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