Tag refinement in an image folksonomy using visual similarity and tag co-occurrence statistics

Noisy tag assignments lower the effectiveness of multimedia applications that rely on the availability of user-supplied tags for retrieving user-contributed images for further processing. This paper discusses a novel tag refinement technique that aims at differentiating noisy tag assignments from correct tag assignments. The correctness of tag assignments is determined through the combined use of visual similarity and tag co-occurrence statistics. To verify the effectiveness of our tag refinement technique, experiments were performed with user-contributed images retrieved from Flickr. For the image set used, the proposed tag refinement technique reduces the number of noisy tag assignments with 36% (benefit), while removing 10% of the correct tag assignments (cost). In addition, our tag refinement technique increases the effectiveness of tag recommendation for non-annotated images with 45% when using the P@5 metric and with 41% when using the NDCG metric.