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Cornell maps the world’s photos
April 23, 2009
Paul Redfern
Read More: http://www.cac.cornell.edu/about/news/090423.aspx
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ITHACA, NY – Cornell University computer scientists used a supercomputer at the Cornell Center for Advanced Computing to download and analyze nearly 35 million Flickr photos taken by over 300,000 photographers from around the globe. Their main goal was to develop new methods to automatically organize and label large-scale collections of digital data. A secondary result of the research was the generation of statistics on the world's most photographed cities and landmarks, gleaned from the analysis of the multi-terabyte photo collection:
• The top 25 most photographed cities in the Flickr data are: (1) New York City (2) London (3) San Francisco (4) Paris (5) Los Angeles (6) Chicago (7) Washington, DC (8) Seattle (9) Rome (10) Amsterdam (11) Boston (12) Barcelona (13) San Diego (14) Berlin (15) Las Vegas (16) Florence (17) Toronto (18) Milan (19) Vancouver (20) Madrid (21) Venice (22) Philadelphia (23) Austin (24) Dublin (25) Portland.
• The top seven most photographed landmarks are: (1) Eiffel Tower - Paris (2) Trafalgar Square - London (3) Tate Modern museum - London (4) Big Ben - London (5) Notre Dame - Paris (6) The Eye - London (7) Empire State Building - New York City.
The study also identified the seven most photographed landmarks in each of the top 25 cities. Most of these landmarks are well-known tourist attractions, but some surprising results emerged. For example, one striking result in the Flickr data is that the Apple Store in midtown Manhattan is the 5th-most photographed place in New York City – and, in fact, the 28th-most photographed place in the world.
Cornell developed techniques to automatically identify places that people find interesting to photograph, showing results for thousands of locations at both city and landmark scales. "We developed classification methods for characterizing these locations from visual, textual and temporal features," says Daniel Huttenlocher, the John P. and Rilla Neafsey Professor of Computing, Information Science and Business and Stephen H. Weiss Fellow. "These methods reveal that both visual and temporal features improve the ability to estimate the location of a photo compared to using just textual tags."
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PDF Reports & Charts: http://www.cs.cornell.edu/~dph/papers/photomap-www09.pdf
April 23, 2009
Paul Redfern
Read More: http://www.cac.cornell.edu/about/news/090423.aspx
##################################
ITHACA, NY – Cornell University computer scientists used a supercomputer at the Cornell Center for Advanced Computing to download and analyze nearly 35 million Flickr photos taken by over 300,000 photographers from around the globe. Their main goal was to develop new methods to automatically organize and label large-scale collections of digital data. A secondary result of the research was the generation of statistics on the world's most photographed cities and landmarks, gleaned from the analysis of the multi-terabyte photo collection:
• The top 25 most photographed cities in the Flickr data are: (1) New York City (2) London (3) San Francisco (4) Paris (5) Los Angeles (6) Chicago (7) Washington, DC (8) Seattle (9) Rome (10) Amsterdam (11) Boston (12) Barcelona (13) San Diego (14) Berlin (15) Las Vegas (16) Florence (17) Toronto (18) Milan (19) Vancouver (20) Madrid (21) Venice (22) Philadelphia (23) Austin (24) Dublin (25) Portland.
• The top seven most photographed landmarks are: (1) Eiffel Tower - Paris (2) Trafalgar Square - London (3) Tate Modern museum - London (4) Big Ben - London (5) Notre Dame - Paris (6) The Eye - London (7) Empire State Building - New York City.
The study also identified the seven most photographed landmarks in each of the top 25 cities. Most of these landmarks are well-known tourist attractions, but some surprising results emerged. For example, one striking result in the Flickr data is that the Apple Store in midtown Manhattan is the 5th-most photographed place in New York City – and, in fact, the 28th-most photographed place in the world.
Cornell developed techniques to automatically identify places that people find interesting to photograph, showing results for thousands of locations at both city and landmark scales. "We developed classification methods for characterizing these locations from visual, textual and temporal features," says Daniel Huttenlocher, the John P. and Rilla Neafsey Professor of Computing, Information Science and Business and Stephen H. Weiss Fellow. "These methods reveal that both visual and temporal features improve the ability to estimate the location of a photo compared to using just textual tags."
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PDF Reports & Charts: http://www.cs.cornell.edu/~dph/papers/photomap-www09.pdf
