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
Generating diverse and representative image search results for landmarks
Proceedings of the 17th international conference on World Wide Web
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CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
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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
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Proceedings of the 18th ACM conference on Information and knowledge management
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Proceedings of the 19th international conference on World wide web
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Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks
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The availability of a huge amount of geotagged resources on the web can be exploited to extract new useful information. We propose a set of estimators that are able to evaluate the degree of clustering of the spatial distribution of terms used to tag such geotagged resources. We introduce the concept of tag point pattern to derive indexes from the exploratory analysis by taking advantage of the second order Ripley's K-function, previously used in epidemiology, geo-statistics and ecology. The derived model estimates the degree of aggregation of the geotagged resources, taking into account the heterogeneity of the spatial distribution of the underlying population. Further, thanks to subsampling techniques, our approach is able to handle large datasets. Without losing of generality, we perform our experiments on a dataset derived Flickr pictures, as a use case. This dataset consists of tags that were extracted from a set of 1.2 million of pictures. We evaluate our proposed indexes with respect to their ability to extract tags related to geographical landmarks and hotspots. Our experiments show that we get good results using our estimators.