Visual summaries of popular landmarks from community photo collections

We present a novel data-driven algorithm that leverages online image repositories such as Flickr for automatically generating tourist maps. Our hypothesis is that, given a large enough dataset of images with geo-based metadata, clusters of matching images from that dataset tend to provide reliable cues as to what the popular tourist spots for that location may be. Our algorithm takes the geographical area of interest as input and retrieves geotagged photos from online photo collections. By clustering the photos based on their locations and identifying the popular tags for each cluster, our algorithm generates a set of points of interest (POIs) for the area. After retrieving additional photos based on these discovered POI tags, we use image matching to find the most representative landmark view for each POI. Finally, we remove clutter from the representative image and apply non-photorealistic rendering techniques to generate a map icon for each landmark. We use our system to automatically generate maps showing popular tourist locations for several large cities.