University of Virginia
images n-k....n Hipi Image
Figure 1: A typical MapReduce pipeline using our Hadoop Image Processing Interface with n images, i map nodes, and j reduce nodes
The amount of images being uploaded to the internet is rapidly increasing, with Facebook users uploading over 2.5 billion new photos every month [Facebook 2010], however, applications that make use of this data are severely lacking. Current computer vision applications use a small …show more content…
2005]. These tasks are typically performed on a distributed system by dividing the task across one or more of the following features: algorithm parameters, images, or pixels [White et al. 2010]. Performing tasks across a particular parameter is incredibly parallel and can often be perfectly parallel. Face detection and landmark classiﬁcation are examples of such algorithms [Li and Crandall. . .
2009; Liu et al. 2009]. The ability to parallelize such tasks allows for scalable, efﬁcient execution of resource-intensive applications.
The MapReduce framework provides a platform for such applications.
Keywords: mapreduce, computer vision, image processing
Basic vision applications that utilize Hadoops MapReduce framework require a staggering learning curve and overwhelming complexity [White et al. 2010]. The overhead required to implement such applications severely cripples the progress of researchers
[White et al. 2010; Li and Crandall. . . 2009]. HIPI removes the highly technical details of Hadoops system and provides users with the familiar feel of an image library with the access to the advanced resources of a distributed system [Dean and Ghemawat