Event Location: NSH 1507
Bio: James Hays received his B.S. in Computer Science from Georgia Institute
of Technology in 2003. He has been a Ph.D. student in Carnegie Mellon
University’s Computer Science Department since 2003 and is advised by
Alexei A. Efros. His research interests are in computer vision and
computer graphics, focusing on image understanding and manipulation
leveraging massive amounts of data. His research has been supported by
a National Science Foundation Graduate Research Fellowship.

Abstract: Estimating geographic information from an image is a difficult
high-level computer vision problem. The emergence of vast amounts of
geographically-calibrated image data is a great reason for computer
vision to start looking globally. We propose a simple algorithm for
estimating a distribution over geographic locations from a single image
using a purely data-driven scene matching approach. For this task, we
will leverage a dataset of over 6 million GPS-tagged images from the
Internet. We represent the estimated image location as a probability
distribution over the Earth’s surface. We quantitatively evaluate our
approach in several geolocation tasks and demonstrate encouraging
performance (up to 30 times better than chance). We show that
geolocation estimates can provide the basis for numerous other image
understanding tasks such as population density estimation, land cover
estimation or urban/rural classification.