This article addresses the issue of social image search result diversification. We propose a novel perspective for the diversification problem via Relevance Feedback (RF). Traditional RF introduces the user in the processing loop by harvesting feedback about the relevance of the search results. This information is used for recomputing a better representation of the data needed. The novelty of our work is in exploiting this concept in a completely automated manner via pseudo-relevance, while pushing in priority the diversification of the results, rather than relevance. User feedback is simulated automatically by selecting positive and negative examples from the initial query results. Unsupervised hierarchical clustering is used then to regroup images according to their content. Diversification is finally achieved with a re-ranking approach. Experimental validation on Flickr data shows the advantages of this approach.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.