Current search engines return relevant results, but often the retrieved items are similar. Moreover, the first images tend to hide all the available richness. In this paper, we propose not only to show how to increase the diversity, but also how to address the hierarchical nature of the diversity. We propose innovative image ordering strategies based on an agglomerative hierarchical clustering (ARC). Furthermore, we introduce a novel approach for exploiting richer description resources, such as a “tree of concepts”. The different approaches are compared on a highly relevant and manually annotated benchmark: the Xilopix benchmark; and on the, more general but less adapted, ImageClef2008 benchmark. Any of the proposed approaches increase the diversity (CR20) compared to search engine's standard outputs and outperform an average random shuffling (baseline). Discussion for each individual novelty is presented. In particular it is show that a hierarchical exploitation of the results of the ARC increases the diversity in all cases.
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