Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
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
SCAN: a structural clustering algorithm for networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
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Proceedings of the 17th international conference on World Wide Web
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
World-scale mining of objects and events from community photo collections
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
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ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Proceedings of the 18th international conference on World wide web
Tour the world: a technical demonstration of a web-scale landmark recognition engine
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Event detection from flickr data through wavelet-based spatial analysis
Proceedings of the 18th ACM conference on Information and knowledge management
Mining tourist information from user-supplied collections
Proceedings of the 18th ACM conference on Information and knowledge management
Learning similarity metrics for event identification in social media
Proceedings of the third ACM international conference on Web search and data mining
City exploration by use of spatio-temporal analysis and clustering of user contributed photos
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Intelligent photo clustering with user interaction and distance metric learning
Pattern Recognition Letters
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We present a technical demonstration of an online city exploration application that helps users identify interesting spots in a city by use of photo clusters corresponding to landmarks and events. Our application, called ClustTour, is based on an efficient landmark and event detection scheme for tagged photo collections. The proposed scheme relies on the combination of a graph-based photo clustering algorithm, making use of both visual and tag information of photos, with a cluster classification and merging module. ClustTour creates a map-based visualization of the identified photo clusters that are classified in prominent categories and are filterable by time and tag. We believe that such an application can greatly facilitate the task of knowing a city through its landmarks and events. So far, the demo has been based on a large photo dataset focused on Barcelona, and it is gradually expanding to contain photo clusters of several major cities of Europe. Furthermore, an Android application is developed that complements the web-based version of ClustTour.