In this paper we address the issue of optimizing the actual social photo retrieval technology in terms of users' requirements. Typical users are interested in taking possession of accurately relevant-to-the-query and non-redundant images so they can build a correct exhaustive perception over the query. We propose to tackle this issue by combining two approaches previously considered non-overlapping: machine image analysis for a pre-filtering of the initial query results followed by crowd-sourcing for a final refinement. In this mechanism, the machine part plays the role of reducing the time and resource consumption allowing better crowd-sourcing results. The machine technique ensures representativeness in images by performing a re-ranking of all images according to the most common image in the initial noisy set; additionally, diversity is ensured by clustering the images and selecting the best ranked images among the most representative in each cluster. Further, the crowd-sourcing part enforces both representativeness and diversity in images, objectives that are, to a certain extent, out of reach by solely the automated machine technique. The mechanism was validated on more than 25,000 photos retrieved from several common social media platforms, proving the efficiency of this approach.
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