Peekaboom: a game for locating objects in images Export

@inproceedings{Peekaboom, 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.}, address = {New York, NY, USA}, author = {von Ahn, Luis and Liu, Ruoran and Blum, Manuel}, booktitle = {CHI '06: Proceedings of the SIGCHI conference on Human Factors in computing systems}, citeulike-article-id = {1090415}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1124782}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1124772.1124782}, doi = {10.1145/1124772.1124782}, isbn = {1-59593-372-7}, keywords = {read}, location = {Montr\'{e}al, Qu\'{e}bec, Canada}, pages = {55--64}, posted-at = {2009-03-25 05:14:24}, priority = {2}, publisher = {ACM}, title = {Peekaboom: a game for locating objects in images}, url = {http://dx.doi.org/10.1145/1124772.1124782}, year = {2006} }

TY - CONF ID - Peekaboom L3 - citeulike-article-id:1090415 N2 - 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. CY - Montr\'{e}al, Qu\'{e}bec, Canada SN - 1-59593-372-7 AD - New York, NY, USA EP - 64 TI - Peekaboom: a game for locating objects in images PB - ACM T2 - CHI '06: Proceedings of the SIGCHI conference on Human Factors in computing systems SP - 55 KW - read AU - von Ahn, Luis AU - Liu, Ruoran AU - Blum, Manuel PY - 2006/// UR - http://dx.doi.org/10.1145/1124772.1124782 ER -