Towards automatic extraction of event and place semantics from flickr tags
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Gazetiki: automatic creation of a geographical gazetteer
Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries
Constructing travel itineraries from tagged geo-temporal breadcrumbs
Proceedings of the 19th international conference on World wide web
Antourage: mining distance-constrained trips from flickr
Proceedings of the 19th international conference on World wide web
Automatic construction of travel itineraries using social breadcrumbs
Proceedings of the 21st ACM conference on Hypertext and hypermedia
Geotagging in multimedia and computer vision--a survey
Multimedia Tools and Applications
STS: complex spatio-temporal sequence mining in flickr
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
The detection of scene features in Flickr
ICWL'10 Proceedings of the 2010 international conference on New horizons in web-based learning
Identifying points of interest by self-tuning clustering
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Proceedings of the 18th Brazilian symposium on Multimedia and the web
LearNext: learning to predict tourists movements
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Uploading tourist photos is a popular activity on photo sharing platforms. These photographs and their associated metadata (tags, geo-tags, and temporal information) should be useful for mining information about the sites visited. However, user-supplied metadata are often noisy and efficient filtering methods are needed before extracting useful knowledge. We focus here on exploiting temporal information, associated with tourist sites that appear in Flickr. From automatically filtered sets of geo-tagged photos, we deduce answers to questions like "how long does it take to visit a tourist attraction?" or "what can I visit in one day in this city?" Our method is evaluated and validated by comparing the automatically obtained visit duration times to manual estimations.