Boost search relevance for tag-based social image retrieval

Social media sharing web sites like Flickr allow users to annotate images with free tags, which greatly facilitate social image search and browsing. However, currently tag-based image search on Flickr does not provide the option of relevance-based ranking, i.e., the search results cannot be ranked according to their relevance levels with respect to the query tag, and this has limited the effectiveness of tagbased search. In this paper, we propose a relevance-based ranking scheme for social image search, aiming to automatically rank images according to their relevance to the query tag. It integrates both the visual consistency between images and the semantic correlation between tags in a unified optimization framework. We propose an iterative method to solve the optimization problem, and the relevancebased ranking can thus be accomplished. Experimental results on real Flickr image collection demonstrate the effectiveness of the proposed approach.