Tagging tags

Social image sharing websites like Flickr have successfully motivated users around the world to annotate images with tags, which greatly facilitate search and organization of social image content. However, these manually-input tags are far from a comprehensive description of the image content, which limits effectiveness of the tags in content-based image search. In this paper, we propose an automatic scheme called tagging tags to supplement semantic image descriptions by associating a group of property tags with each existing tag. For example, an initial tag "tiger" will be further tagged with "white", "stripes" and "bottom-right" along three tag properties: color, texture and location, respectively. In the proposed scheme, a lazy learning approach is first applied to estimate the corresponding image regions of each initial tag, and then a set of property tags, which involve six exemplary property aspects including location, color, texture, shape, size and dominance, are derived for each tag according to the content of the regions and the entire image. These tag properties enable much more precise image search especially when certain tag properties are included in the query. The results of the empirical evaluation show that tag properties remarkably boost the performance of social image retrieval.