Article

Peekaboom: a game for locating objects in images

Authors:

Luis von Ahn,

Ruoran Liu,

Manuel BlumAuthors Info & Claims

CHI '06: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Pages 55 - 64

https://doi.org/10.1145/1124772.1124782

Published: 22 April 2006 Publication History

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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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Mandryk RMirza-Babaei PDenisova AFreeman GJohnson D(2024)Games and Play SIG: Connecting Games Research to the Broader HCI ContextExtended Abstracts of the CHI Conference on Human Factors in Computing Systems10.1145/3613905.3643986(1-6)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613905.3643986
Yang YShin AKim NWoo HChung JSong J(2024)Find the Bot!: Gamifying Facial Emotion Recognition for Both Human Training and Machine Learning Data CollectionProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642880(1-20)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642880
Aliady WPoesio M(2024)Master the Linguistic Landscape: Puzzle Integration in a 3D NLP Game2024 IEEE Conference on Games (CoG)10.1109/CoG60054.2024.10645588(1-8)Online publication date: 5-Aug-2024
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Index Terms

Peekaboom: a game for locating objects in images
1. Computing methodologies
  1. Machine learning
2. Human-centered computing
  1. Collaborative and social computing

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Information

Published In

CHI '06: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

April 2006

1353 pages

ISBN:1595933727

DOI:10.1145/1124772

Editors:
Rebecca Grinter
Georgia Institute of Technology, USA
,
Thomas Rodden
University of Nottingham, UK
,
Paul Aoki
Intel, USA
,
Ed Cutrell
Microsoft, USA
,
Robin Jeffries
Google, USA
,
Gary Olson
University of Michigan, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 April 2006

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CHI06: CHI 2006 Conference on Human Factors in Computing Systems

April 22 - 27, 2006

Québec, Montréal, Canada

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Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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