An evaluation of nearest-neighbor methods for tag refinement

The success of media sharing and social networks has led to the availability of extremely large quantities of images that are tagged by users. The need of methods to manage efficiently and effectively the combination of media and metadata poses significant challenges. In particular, automatic image annotation of social images has become an important research topic for the multimedia community. In this paper we propose and thoroughly evaluate the use of nearest-neighbor methods for tag refinement. Extensive and rigorous evaluation using two standard large-scale datasets shows that the performance of these methods is comparable with that of more complex and computationally intensive approaches and that, differently from these latter approaches, nearest-neighbor methods can be applied to `web-scale' data.