Visual summary of popular landmarks

This projects consists in creating tourist maps leveraging the large collection of geolocated Flickr images.

I developed all the scripts in Python to retrieve and extract popular landmarks by clustering image references using the K-means algorithm. The image processing elements, and the map itself were handled by the co-authors.


W. Chen, A. Battestini, N. Gelfand, V. Setlur, “Visual Summaries of Popular Landmarks from Community Photo Collections”, ACM Multimedia 2009. (video)


Flickr has a large set of geo-located images. We used the assumption that the more popular a landmark, the more photos would be taken of it.

I wrote a series of scripts to fetch all Flickr images taken within a geographical boundary, and cluster those images using K-means and TF-IDF. The result was a set of cluster geographically located, and labeled with the landmark tags.

The co-authors used several image processing components to merge different images of the same landmark, and create a thumbnail for each landmark. Those landmarks thumbnails were then placed on a map image.

Watch the video to see the process in action.

Final tourist map for San Francisco

Every Flickr image found geolocated in San Francisco is shown. The color represents the cluster to which it belongs.

A cropped image shows a subset of the final tag for each cluster is shown for San Francisco. Clusters are ranked in order of importance. Tags are like: GoldenGateBridge, Alcatraz, PacificHeights, etc

I ran the process through different cities, but San Francisco was definitely the most successful one because it had so many more geolocated photos in 2008-2009 than other cities.