Point of interest mining with proper semantic annotation

Mining geo-tagged social photo media has received large amounts of attention from researchers recently. Points of interest (POI) mining from a collection of geo-tagged photos is one of these problems. POI mining refers to the processes of pattern recognition (namely clustering), extraction and semantic annotation. However, based on unsupervised clustering methods, many POIs might not be mined. Additionally, there is a great challenge for the proper semantic annotation to data clusters after clustering. In practice, there are many applications which require the accuracy of semantic annotation and high quality of pattern recognition such as POI recommendation. In this paper, we study POI mining from a collection of geo-tagged photos in combination with proper semantic annotation by using additional POI information from high coverage external POI databases. We propose a novel POI mining framework by using two-level clustering, random walk and constrained clustering. In random walk clustering step, we separate a large-scale collection of geo-tagged photos into many clusters. In the constrained clustering step, we continue to divide the clusters that include many POIs into many sub-clusters, where the geo-tagged photos in a sub-cluster associate with a particular POI. Experimental results on two datasets of geo-tagged Flickr photos of two cities in California, USA have shown that the proposed method substantially outperforms existing approaches that are adapted to handle the problem.