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| IM2GPS: estimating geographic information from a single image |
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Google Tech Talks
August 5, 2008
ABSTRACT
Estimating geographic information from an
image is an excellent, difficult high-level
computer vision problem whose time has come.
The emergence of vast amounts of
geographically-calibrated image data is a
great reason for computer vision to start
looking globally on the scale of the entire
planet! In this paper, 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.
Speaker: James Hays
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.
Tags : google techtalks techtalk engedu talk talks googletechtalks education |
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Affichage : 1447
Durée : 2846 s |
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