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A Ranking based Model for Automatic Annotation in a Social Network

Abstract

We propose a relational ranking model for learning to tag images in social media sharing systems. This model learns to associate a ranked list of tags to unlabeled images, by considering simultaneously content information (visual or textual) and relational information among the images. It is able to handle implicit relations like content similarities, and explicit ones like friendship or authorship. The model itself is based on a transductive algorithm thats learns from both labeled and unlabeled data. Experiments on a real corpus extracted from Flickr show the effectiveness of this model.
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Dates and versions

hal-01292064 , version 1 (22-03-2016)

Identifiers

  • HAL Id : hal-01292064 , version 1

Cite

Ludovic Denoyer, Patrick Gallinari. A Ranking based Model for Automatic Annotation in a Social Network. The Fourth International AAAI Conference on Weblogs and Social Media, ICWSM 2010, May 2010, Washington, DC, United States. pp.231-234. ⟨hal-01292064⟩
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