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Re-Ranking the Image Search Results for Relevance and Diversity in MediaEval 2014 Challenge

In this paper we introduce a renement and diversication process for re-ranking image search results based on social metadata and visual characteristics of the photos. The goal of the developed re-ranking algorithm is to construct a new sequence with maximal value of the harmonic mean of precision and diversity. Our contribution is twofold: estimation of precision using the statistical average and mixing of clustering results in order to get better diversity. In the combined clustering the new label set is the Cartesian product of the two original cluster label sets.