A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
Why we tag: motivations for annotation in mobile and online media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
World explorer: visualizing aggregate data from unstructured text in geo-referenced collections
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Neighborhood restrictions in geographic IR
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Generating diverse and representative image search results for landmarks
Proceedings of the 17th international conference on World Wide Web
Automatically refining the wikipedia infobox ontology
Proceedings of the 17th international conference on World Wide Web
Spirittagger: a geo-aware tag suggestion tool mined from flickr
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Methods for extracting place semantics from Flickr tags
ACM Transactions on the Web (TWEB)
Proceedings of the 18th international conference on World wide web
Placing flickr photos on a map
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Automatic tagging and geotagging in video collections and communities
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Methods for extracting place semantics from Flickr tags
ACM Transactions on the Web (TWEB)
Automatic tagging and geotagging in video collections and communities
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Geo-Location estimation of flickr images: social web based enrichment
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
A visual approach for video geocoding using bag-of-scenes
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Placing images on the world map: a microblog-based enrichment approach
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
State of the Geotag: where are we?
Proceedings of the ACM multimedia 2012 workshop on Geotagging and its applications in multimedia
Web Semantics: Science, Services and Agents on the World Wide Web
Multimedia multimodal geocoding
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
@Phillies Tweeting from Philly? Predicting Twitter User Locations with Spatial Word Usage
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Detecting Places of Interest Using Social Media
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Geo-visual ranking for location prediction of social images
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
Georeferencing Flickr resources based on textual meta-data
Information Sciences: an International Journal
Uncovering locally characterizing regions within geotagged data
Proceedings of the 22nd international conference on World Wide Web
Discovering and Characterizing Places of Interest Using Flickr and Twitter
International Journal on Semantic Web & Information Systems
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We present a two-step approach to estimate where a given photo or video was taken, using only the tags that a user has assigned to it. In the first step, a language modeling approach is adopted to find the area which most likely contains the geographic location of the resource. In the subsequent second step, a precise location is determined within the area that was found to be most plausible. The main idea of this step is to compare the multimedia object under consideration with resources from the training set, for which the exact coordinates are known, and which were taken in that area. Our final estimation is then determined as a function of the coordinates of the most similar among these resources. Experimental results show this two-step approach to improve substantially over either language models or similarity search alone.