Learning to Recommend Tags for On-line Photos

  • Zheshen Wang
  • Baoxin Li
Conference paper


Recommending text tags for on-line photos is useful for on-line photo services. We propose a novel approach to tag recommendation by utilizing both the underlying semantic correlation between visual contents and text tags and the tag popularity learnt from realistic on-line photos. We apply our approach to a database of real on-line photos and evaluate its performance by both objective and subjective evaluation. Experiwith ments demonstrate the improved performance of the proposed approach compared the state-of-the-art techniques in the literature.


Latent Semantic Analysis Collaborative Filter Visual Content Computer Support Cooperative Work Fuzzy Association Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag US 2009

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringArizona State UniversityTempe

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