  This paper aims at discovering meaningful subsets of related images from
large image collections without annotations. We search groups of images related
at different levels of semantic, i.e., either instances or visual classes.
While k-means is usually considered as the gold standard for this task, we
evaluate and show the interest of diffusion methods that have been neglected by
the state of the art, such as the Markov Clustering algorithm.
  We report results on the ImageNet and the Paris500k instance dataset, both
enlarged with images from YFCC100M. We evaluate our methods with a labelling
cost that reflects how much effort a human would require to correct the
generated clusters.
  Our analysis highlights several properties. First, when powered with an
efficient GPU implementation, the cost of the discovery process is small
compared to computing the image descriptors, even for collections as large as
100 million images. Second, we show that descriptions selected for instance
search improve the discovery of object classes. Third, the Markov Clustering
technique consistently outperforms other methods; to our knowledge it has never
been considered in this large scale scenario.
