The wide adoption of photo sharing applications such as Flickr
and the massive amounts of user-generated content uploaded to them
raises an information overload issue for users. An established technique
to overcome such an overload is to cluster images into groups
based on their similarity and then use the derived clusters to assist
navigation and browsing of the collection. In this paper, we present
a community detection (i.e. graph-based clustering) approach that
makes use of both visual and tagging features of images in order
to efficiently extract groups of related images within large image
collections. Based on experiments we conducted on a dataset comprising
publicly available images from Flickr, we demonstrate the
efficiency of our method, the added value of combining visual and
tag features and the utility of the derived clusters for exploring an
image collection.