NDLI: Geo-location inference from image content and user tags

Content Provider	IEEE Xplore Digital Library
Author	Gallagher, A. Joshi, D. Jie Yu Jiebo Luo
Copyright Year	2009
Description	Author affiliation: Kodak Res. Labs., Rochester, NY, USA (Gallagher, A.; Joshi, D.; Jie Yu; Jiebo Luo)
Abstract	Associating image content with their geographic locations has been increasingly pursued in the computer vision community in recent years. In a recent work, large collections of geotagged images were found to be helpful in estimating geo-locations of query images by simple visual nearest-neighbors search. In this paper, we leverage user tags along with image content to infer the geo-location. Our model builds upon the fact that the visual content and user tags of pictures can provide significant hints about their geo-locations. Using a large collection of over a million geotagged photographs, we build location probability maps of user tags over the entire globe. These maps reflect the picture-taking and tagging behaviors of thousands of users from all over the world, and reveal interesting tag map patterns. Visual content matching is performed using multiple feature descriptors including tiny images, color histograms, GIST features, and bags of textons. The combination of visual content matching and local tag probability maps forms a strong geo-inference engine. Large-scale experiments have shown significant improvements over pure visual content-based geo-location inference.
Starting Page	55
Ending Page	62
File Size	574001
Page Count	8
File Format	PDF
ISBN	9781424439942
ISSN	21607508
DOI	10.1109/CVPRW.2009.5204168
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer vision Histograms Image databases Laboratories Humans Search engines Tagging Large-scale systems Global Positioning System Nearest neighbor searches
Content Type	Text
Resource Type	Article

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