Learning to recommend tags for on-line photos

Zheshen Wang, Baoxin Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

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. Experiments demonstrate the improved performance of the proposed approach compared with the state-of-the-art techniques in the literature.

Original languageEnglish (US)
Title of host publicationSocial Computing and Behavioral Modeling
Pages227-235
Number of pages9
DOIs
StatePublished - Dec 1 2009
Event2nd International Workshop on Social Computing, Behavioral Modeling and Prediction, SBP 2009 - Phoenix, AZ, United States
Duration: Mar 31 2009Apr 1 2009

Publication series

NameSocial Computing and Behavioral Modeling

Other

Other2nd International Workshop on Social Computing, Behavioral Modeling and Prediction, SBP 2009
CountryUnited States
CityPhoenix, AZ
Period3/31/094/1/09

ASJC Scopus subject areas

  • Modeling and Simulation

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  • Cite this

    Wang, Z., & Li, B. (2009). Learning to recommend tags for on-line photos. In Social Computing and Behavioral Modeling (pp. 227-235). (Social Computing and Behavioral Modeling). https://doi.org/10.1007/978-1-4419-0056-2-29