OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Depth Estimation from Image Structure
IEEE Transactions on Pattern Analysis and Machine Intelligence
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Indoor-Outdoor Image Classification
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
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
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
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Mining the web for points of interest
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
| Hi-index | 0.00 |
Points of interest (POIs) are a core component of geographical databases and of location based services. POI acquisition was performed by domain experts but associated costs and access difficulties in many regions of the world reduce the coverage of manually built geographical databases. With the availability of large geotagged multimedia datasets on the Web, a sustained research effort was dedicated to automatic POI discovery and characterization. However, in spite of its practical importance, POI localization was only marginally addressed. To compute POI coordinates an assumption was made that the more data were available, the more precise the localization will be. Here we shift the focus of the process from data quantity to data quality. Given a set of geotagged Flickr photos associated to a POI, close-up classification is used to trigger a spatial clustering process. To evaluate the newly introduced method against different other localization schemes, we create an accurate ground truth. We show that significant localization error reductions are obtained compared to a coordinate averaging approach and to a X-Means clustering scheme.