Automatic organization for digital photographs with geographic coordinates
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Temporal event clustering for digital photo collections
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
VirtualTour: an online travel assistant based on high quality images
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Why we tag: motivations for annotation in mobile and online media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Methods for extracting place semantics from Flickr tags
ACM Transactions on the Web (TWEB)
Proceedings of the 18th international conference on World wide web
Mining city landmarks from blogs by graph modeling
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Generating location overviews with images and tags by mining user-generated travelogues
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Automatic generation of multimedia tour guide from local blogs
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
Mining and visualizing local experiences from blog entries
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
Automated event clustering and quality screening of consumer pictures for digital albuming
IEEE Transactions on Multimedia
Methods for extracting place semantics from Flickr tags
ACM Transactions on the Web (TWEB)
Finding media illustrating events
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Personalized travel recommendation by mining people attributes from community-contributed photos
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Multi-video summary and skim generation of sensor-rich videos in geo-space
Proceedings of the 3rd Multimedia Systems Conference
TripRec: recommending trip routes from large scale check-in data
Proceedings of the 21st international conference companion on World Wide Web
Exploiting large-scale check-in data to recommend time-sensitive routes
Proceedings of the ACM SIGKDD International Workshop on Urban Computing
People search and activity mining in large-scale community-contributed photos
Proceedings of the 20th ACM international conference on Multimedia
Generating tourism path from trajectories and geo-photos
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
Geo topic model: joint modeling of user's activity area and interests for location recommendation
Proceedings of the sixth ACM international conference on Web search and data mining
Photographer paths: sequence alignment of geotagged photos for exploration-based route planning
Proceedings of the 2013 conference on Computer supported cooperative work
Discovering local attractions from geo-tagged photos
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Nontrivial landmark recommendation using geotagged photos
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Sections on Paraphrasing; Intelligent Systems for Socially Aware Computing; Social Computing, Behavioral-Cultural Modeling, and Prediction
Personalized intra- and inter-city travel recommendation using large-scale geotags
Proceedings of the 2nd ACM international workshop on Geotagging and its applications in multimedia
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Photo sharing is one of the most popular Web services. Photo sharing sites provide functions to add tags and geo-tags to photos to make photo organization easy. Considering that people take photos to record something that attracts them, geo-tagged photos are a rich data source that reflects people's memorable events associated with locations. In this paper, we focus on geo-tagged photos and propose a method to detect people's frequent trip patterns, i.e., typical sequences of visited cities and durations of stay as well as descriptive tags that characterize the trip patterns. Our method first segments photo collections into trips and categorizes them based on their trip themes, such as visiting landmarks or communing with nature. Our method mines frequent trip patterns for each trip theme category. We crawled 5.7 million geo-tagged photos and performed photo trip pattern mining. The experimental result shows that our method outperforms other baseline methods and can correctly segment photo collections into photo trips with an accuracy of 78%. For trip categorization, our method can categorize about 80% of trips using tags and titles of photos and visited cities as features. Finally, we illustrate interesting examples of trip patterns detected from our dataset and show an application with which users can search frequent trip patterns by querying a destination, visit duration, and trip theme on the trip.