Abstract
Information management systems today face a tremendous challenge considering the growing popularity of social media repositories involving images and video. Considering the growing volume of multimedia content in such online media-sharing communities there is an increasing need for novel ways of organizing content. In this paper we consider the problem of organizing images in a given Flickr Group by discovering latent subgroups. A Flickr Group can be visualized as a collection of such subgroups where each subgroup represents a distinct theme. We model the task of discovering subgroups as that of finding highly correlated topics from a dataset containing images and associated tags. The proposed probabilistic model employs a more flexible prior distribution to model topic-topic correlations and utilizes both tag and image information for discovering such subgroups. Our experiments on Flickr Group data demonstrate that the model is able to successfully discover subgroups without any supervision. © 2012 IEEE.