Inferring generic activities and events from image content and bags of geo-tags
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
A critical evaluation of location based services and their potential
Journal of Location Based Services
Event recognition: viewing the world with a third eye
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Identifying Meaningful Places: The Non-parametric Way
Pervasive '08 Proceedings of the 6th International Conference on Pervasive Computing
A similarity search of trajectory data using textual information retrieval techniques
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
Modeling people's place naming preferences in location sharing
Proceedings of the 12th ACM international conference on Ubiquitous computing
Mining significant semantic locations from GPS data
Proceedings of the VLDB Endowment
Geotagging in multimedia and computer vision--a survey
Multimedia Tools and Applications
Hyper-local, directions-based ranking of places
Proceedings of the VLDB Endowment
Context-aware apps with the Zonezz platform
MobiHeld '11 Proceedings of the 3rd ACM SOSP Workshop on Networking, Systems, and Applications on Mobile Handhelds
Semantic trajectory mining for location prediction
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Transportation mode detection using mobile phones and GIS information
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Inferring photographic location using geotagged web images
Multimedia Tools and Applications
Feature engineering for semantic place prediction
Pervasive and Mobile Computing
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With help of context, computer systems and applications could be more user-friendly, flexible and adaptable. With semantic locations, applications can understand users better or provide helpful services. We propose a method that automatically derives semantic locations from user's trace. Our experimental results show that the proposed method identities up to 96% correct semantic locations.