Image clustering through community detection on hybrid image similarity graphs

Abstract

The wide adoption of photo sharing applications such as Flickr <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">©</sup> 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 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">©</sup> , 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.