Abstract
In this paper we address the problem of user-adapted image retrieval. First, we provide a survey of the performance of the existing social media retrieval platforms and highlight their limitations. In this context, we propose a hybrid, two step, machine and human automated media analysis approach. It aims to improve retrieval relevance by selecting a small number of representative and diverse images from a noisy set of candidate images (e.g. the case of Internet media). In the machine analysis step, to ensure representativeness, images are re-ranked according to the similarity to the “most common” image in the set. Further, to ensure also the diversity of the results, images are clustered and the best ranked images among the most representative in each cluster are retained. The human analysis step aims to bridge further inherent descriptor semantic gap. The retained images are further refined via crowd-sourcing which adapts the results to human. The method was validated in the context of the retrieval of images with monuments using a data set of more than 25.000 images retrieved from various social image search platforms.
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Acknowledgments
This research is partially supported by the CUbRIK project, an IP funded within the FP7/2007–2013 under grant agreement n287704 and by the Romanian Sectoral Operational Programme Human Resources Development 2007–2013 through the Financial Agreement POSDRU/89/1.5/S/62557.
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Radu, AL., Stöttinger, J., Ionescu, B., Menéndez, M., Giunchiglia, F. (2014). Representativeness and Diversity in Photos via Crowd-Sourced Media Analysis. In: Nürnberger, A., Stober, S., Larsen, B., Detyniecki, M. (eds) Adaptive Multimedia Retrieval: Semantics, Context, and Adaptation. AMR 2012. Lecture Notes in Computer Science(), vol 8382. Springer, Cham. https://doi.org/10.1007/978-3-319-12093-5_6
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DOI: https://doi.org/10.1007/978-3-319-12093-5_6
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