Scalable Mining of Objects and Events from Community Photo Collections
Till Quack
(ETH Zurich, Switzerland)
We describe an approach for mining images of objects (such as landmark
buildings) from community photo collections in an unsupervised fashion.
Our approach relies on retrieving geotagged photos from those web-sites
using a grid of geospatial tiles. The downloaded photos are clustered
into potentially interesting entities through a processing pipeline of
several modalities, including visual, textual and spatial proximity. The
resulting clusters are analyzed and are automatically classified into
objects and events. Using data mining techniques, we then find text
labels for these clusters, which are used to again assign each cluster
to a corresponding Wikipedia article in a fully unsupervised manner. A
final verification step uses the contents (including images) from the
selected Wikipedia article to verify the cluster-article assignment.
We demonstrate this approach on several urban areas, densely covering an
area of over 700 square kilometers and mining over 200,000 photos.