A hybrid machine-crowd approach to photo retrieval result diversification

Radu, A. L. and Ionescu, B. and Menéndez, M. F. and Stöttinger, J. and Giunchiglia, F. and De angeli, A. (2014) A hybrid machine-crowd approach to photo retrieval result diversification. In: 20th Anniversary International Conference on MultiMedia Modeling, 6-10 January 2014, Dublin, Ireland.

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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive

Abstract

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.

Keywords:Automated machines, crowd-sourcing, Image content, Photo retrieval, Resource consumption, results diversification, Social media, Social media platforms, Artificial intelligence, Computer science, Computers, Image retrieval
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:25471
Deposited On:14 Aug 2017 09:43

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