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Geotag propagation in social networks based on user trust model

Ivanov, Ivan ; Vajda, Peter ; Lee, Jong-Seok ; Goldmann, Lutz ; Ebrahimi, Touradj

In: Multimedia Tools and Applications, 2012, vol. 56, no. 1, p. 155-177

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    Titre
    • Geotag propagation in social networks based on user trust model
    Auteur
    • Ivanov, Ivan. Multimedia Signal Processing Group (MMSPG), Institute of Electrical Engineering (IEL), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015, Lausanne, Switzerland
    • Vajda, Peter. Multimedia Signal Processing Group (MMSPG), Institute of Electrical Engineering (IEL), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015, Lausanne, Switzerland
    • Lee, Jong-Seok. Multimedia Signal Processing Group (MMSPG), Institute of Electrical Engineering (IEL), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015, Lausanne, Switzerland
    • Goldmann, Lutz. Multimedia Signal Processing Group (MMSPG), Institute of Electrical Engineering (IEL), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015, Lausanne, Switzerland
    • Ebrahimi, Touradj. Multimedia Signal Processing Group (MMSPG), Institute of Electrical Engineering (IEL), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015, Lausanne, Switzerland
    Type de document
    • Postprint
    Langue
    • Anglais
    Publié dans
    • Multimedia Tools and Applications, 2012, vol. 56, no. 1, p. 155-177. Springer US; http://www.springer-ny.com
    Autre version électronique
    Identifiant OAI-PMH
    • oai:doc.rero.ch:322000
    Summary
    In the past few years sharing photos within social networks has become very popular. In order to make these huge collections easier to explore, images are usually tagged with representative keywords such as persons, events, objects, and locations. In order to speed up the time consuming tag annotation process, tags can be propagated based on the similarity between image content and context. In this paper, we present a system for efficient geotag propagation based on a combination of object duplicate detection and user trust modeling. The geotags are propagated by training a graph based object model for each of the landmarks on a small tagged image set and finding its duplicates within a large untagged image set. Based on the established correspondences between these two image sets and the reliability of the user, tags are propagated from the tagged to the untagged images. The user trust modeling reduces the risk of propagating wrong tags caused by spamming or faulty annotation. The effectiveness of the proposed method is demonstrated through a set of experiments on an image database containing various landmarks