We present a technical demonstration of an online city exploration application that helps users identify interesting spots in a city by use of spatio-temporal analysis and clustering of user contributed photos. Our framework analyzes the spatial distribution of large city-centered collections of user contributed photos at different time scales in order to index the most popular spots of a city in a time-aware manner. Subsequently, the photo sets belonging to the same spatiotemporal context are clustered in order to extract representative photos for each spot. The resulting application enables users to obtain flexible summaries of the most important spots in a city given a temporal slice (time of the day, month, season). The demonstration will be based on a photo dataset covering major European cities.

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.