Tag Relevance for Social Image Retrieval in Accordance with Neighbor Voting Algorithm

Social image retrieval is important for exploiting the increasing amounts of amateur-tagged multimedia such as Flickr images. Intuitively, if different persons label similar images using the same tags, these tags are likely to reflect objective aspects of the visual content. Interpreting the relevance of a user-contributed tag with respect to the visual content of an image is an emerging problem in social image retrieval. An algorithm is proposed that scalably and reliably learns tag relevance by accumulating votes from visually similar neighbours. Treated as tag frequency, learned tag relevance is seamlessly embedded into current tagbased social image retrieval paradigms. Preliminary experiments on two thousand Flickr images demonstrate the potential of the proposed algorithm. The tag relevance learning algorithm substantially improves upon baselines for all the experiments. The results suggest that the proposed algorithm is promising for real-world applications.