@conference {13086,
	title = {Web-scale computer vision using MapReduce for multimedia data mining},
	booktitle = {Proceedings of the Tenth International Workshop on Multimedia Data Mining},
	series = {MDMKDD {\textquoteright}10},
	year = {2010},
	month = {2010///},
	pages = {9:1{\textendash}9:10 - 9:1{\textendash}9:10},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {This work explores computer vision applications of the MapReduce framework that are relevant to the data mining community. An overview of MapReduce and common design patterns are provided for those with limited MapReduce background. We discuss both the high level theory and the low level implementation for several computer vision algorithms: classifier training, sliding windows, clustering, bag-of-features, background subtraction, and image registration. Experimental results for the k-means clustering and single Gaussian background subtraction algorithms are performed on a 410 node Hadoop cluster.},
	keywords = {background subtraction, bag-of-features, Cloud computing, clustering, Computer vision, image registration, MapReduce},
	isbn = {978-1-4503-0220-3},
	doi = {10.1145/1814245.1814254},
	url = {http://doi.acm.org/10.1145/1814245.1814254},
	author = {White,Brandyn and Tom Yeh and Jimmy Lin and Davis, Larry S.}
}
