Supervised text-based geolocation using language models on an adaptive grid
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
GeoMM'12: ACM international workshop on geotagging and its applications in multimedia
Proceedings of the 20th ACM international conference on Multimedia
Full 6DOF pose estimation from geo-located images
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
GIANT: geo-informative attributes for location recognition and exploration
Proceedings of the 21st ACM international conference on Multimedia
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Image-based location estimation methods typically recognize every photo independently, and their resulting reliance on strong visual feature matches makes them most suited for distinctive landmark scenes. We observe that when touring a city, people tend to follow common travel patterns - for example, a stroll down Wall Street might be followed by a ferry ride, then a visit to the Statue of Liberty. We propose an approach that learns these trends directly from online image data, and then leverages them within a Hidden Markov Model to robustly estimate locations for novel sequences of tourist photos. We further devise a set-to-set matching-based likelihood that treats each "burst" of photos from the same camera as a single observation, thereby better accommodating images that may not contain particularly distinctive scenes. Our experiments with two large datasets of major tourist cities clearly demonstrate the approach's advantages over methods that recognize each photo individually, as well as a simpler HMM baseline that lacks the proposed burst-based observation model.