Pseudo-relevance feedback diversification of social image retrieval results

In this paper we introduce a novel pseudo-relevance feedback (RF) perspective to social image search results diversification. Traditional RF techniques introduce the user in the processing loop by harvesting feedback about the relevance of the query results. This information is used for recomputing a better representation of the needed data. The novelty of our work is in exploiting the automatic generation of user feedback in a completely unsupervised diversification scenario, where positive and negative examples are used to generate better representations of visual classes in the data. First, user feedback is simulated automatically by selecting positive and negative examples from the initial query results. Then, an unsupervised hierarchical clustering is used to re-group images according to their content. Diversification is finally achieved with a re-ranking approach of the previously achieved clusters. Experimental validation on real-world data from Flickr shows the benefits of this approach achieving very promising results.