technique could simplify mapping remote archaeological sites,
4 where using traditional laser range scanners is
expensive and challenging. A cheaper
and simpler alternative would be to
use a digital camera to take many photos of a site, and then run our reconstruction algorithms on those photos
once the researchers return from the
field. As another example, we have
recently studied how to automatically
mine online photo collections for images of natural phenomena like snowfall and flowering, potentially giving
ecologists a new technique for collecting observational data at a continental scale.
Imagine all of the world’s photos
as coming from a “distributed camera,” continually capturing images all
around the world. Can this camera be
calibrated to estimate the place and
time each of these photos was taken?
If so, we could start building a new
kind of image search and analysis
tool—one that would, for example,
allow a scientist to find all images
of Central Park over time in order to
study changes in flowering times from
year to year, or that would allow an engineer to find all available photos of a
particular bridge online to determine
why it collapsed. Gaining true understanding of the world from the sea of
photos online could have a truly transformative impact.
An earlier version of this article was
presented as a keynote talk at Arts | Hu-manities | Complex Networks—A Leonardo satellite symposium at NetSci2010
Photoshop Scalability: Keeping It Simple
Clem Cole, Russell Williams
Unifying Biological Image
Formats with hDF5
Matthew T. Dougherty et al.
Document & Media Exploitation
Simson L. Garfinkel
1. agarwal, s., snavely, n., simon, I., seitz, s., and
szeliski, r. Building Rome in a day. International
Conference on Computer Vision (2009).
2. the app Garden. 2010; http://www.flickr.com/
3. building rome in a day; http://grail.cs.washington.edu/
This article has
of our initial work
into unlocking the
information latent in
Web sites using
algorithms, but the
true promise of this
type of analysis is
yet to be realized.
4. Chen, X., Morvan, y., He, y., dorsey, J. and rushmeier,
H. an integrated image and sketching environment
for archaeological sites. Workshop on Applications of
Computer Vision in Archaeology, (2010).
5. Comaniciu, d. and Meer, P. Mean shift: a robust
approach toward feature space analysis. IEEE
Transactions on Pattern Analysis and Machine
Intelligence 24, 5 (2002).
6. Crandall, d., backstrom, l., Cosley, d., suri, s.,
Huttenlocher, d. and kleinberg, J. Inferring social
ties from geographic coincidences. Proceedings of
the National Academy of Science 107, 52 (2010),
7. Crandall, d., backstrom, l., Huttenlocher, d.
and kleinberg, J. Mapping the world’s photos.
International World Wide Web Conference (2009).
8. Crandall, d., backstrom, l., Huttenlocher, d., and
kleinberg, J. Mapping the world’s photos; http://www.
9. Crandall, d., owens, a., snavely, n. and Huttenlocher,
d. discrete-continuous optimization for large-scale
structure from motion. Conference on Computer Vision
and Pattern Recognition (2011), 3001–3008.
10. Crandall, d., owens, a., snavely, n., Huttenlocher,
d. discrete-continuous optimization for large-scale
structure from motion, (2011); http://vision.soic.
11. Flickr. 6,000,000,000; http://blog.flickr.net/
12. Hartley, r. and zisserman, a. Multiple View Geometry
in Computer Vision. Cambridge university Press,
Cambridge, Ma, 2003.
13. lazer, d., Pentland, a., adamic, l., aral, s., barabasi,
a.-l., brewer, d., Christakis, n., Contractor, n., Fowler,
J., Gutmann, M., Jebara, t., king, G., Macy, M., roy, d.
and Van alstyne, M. Computational social science.
Science 323, 5915 (2009), 721–723.
14. lowe, d.G. distinctive image features from scale-invariant keypoints. International Journal in Computer
15. Melki, s. Photo reproduced under a Creative-
Commons-attribution license. Flickr user
16. Michel, J.-b., shen, y.k., aiden, a.P., Veres, a. and Gray,
M.k., the Google books team, Pickett, J. P., Hoiberg, d.,
Clancy, d., norvig, P., or want, J., Pinker, s., nowak, M.,
lieberman-aiden, e. quantitative analysis of culture
using millions of digitized books. Science 331, 6014
17. Milgram, s. Psychological maps of Paris. In
Environmental Psychology: People and Their Physical
Settings. H.M. Proshansky, W. H. Ittelson, and l. G.
rivlin, eds. Holt, rinehart and Winston, new york, ny,
18. Pearl, J. Probabilistic Reasoning in Intelligent
Systems: Net works of Plausible Inference. Morgan
kaufmann Publishers, san Mateo, Ca, 1988.
19. Photo tourism; http://phototour.cs.washington.edu/.
20. shaffer, J. bigger, faster photos. the Facebook
blog (2011); http://blog.facebook.com/blog.
21. snavely, n., seitz, s. and szeliski, r. Modeling the
world from Internet photo collections. International
Journal of Computer Vision 80, 2 (2008).
22. tuite, k., snavely, n., Hsiao, d.-y., tabing, n., and
Popović, z. PhotoCity: training experts at large-scale
image acquisition through a competitive game.
Conference on Human Factors in Computing (2011).
23. zhang, H., korayem, M., Crandall, d., and lebuhn, G.
Mining Photo-sharing Websites to study ecological
Phenomena. International World Wide Web
David Crandall is an assistant professor in the school
of Informatics and Computing at Indiana university in
bloomington, In. His research interests are computer
vision and data mining, with a focus on visual object
recognition, image understanding, machine learning, and
mining and modeling of complex net works.
noah Snavely is an assistant professor of computer
science at Cornell university, where he has been on the
faculty since 2009. snavely works in computer graphics
and computer vision, with a particular interest in using
vast amounts of imagery from the Internet to reconstruct
and visualize the world in 3d, and in creating new tools for
enabling people to capture and share their environments.
© 2012 aCM 0001-0782/12/06 $10.00