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Multimedia Systems

, Volume 22, Issue 4, pp 395–404 | Cite as

Geo-location driven image tagging via cross-domain learning

  • Weizhi Nie
  • Anan LiuEmail author
  • Zhongyang Wang
  • Yuting Su
Special Issue Paper

Abstract

With the rapid development of location-based social network, more and more multimedia data are uploaded by users. These data always include large-scale of independent information with both textual and visual contents. To bridge the semantic gap in between, we propose a novel cross-domain learning method for automatic image annotation with geo-location information. First, we propose the topic model-based method for popular concept extraction to adaptively construct cross-domain datasets. Then these concepts are utilized to collect the visual correlation information from Flickr. Finally, we leverage cross-domain learning method for model learning. The comparison experiments on cross-domain datasets are conducted to demonstrate the superiority of the proposed method.

Keywords

Location-based social network Image annotation Cross-domain data Machine learning Social media 

Notes

Acknowledgments

I would like to express my deep gratitude to Prof. Tat-Seng Chua and the NeXT group in National University of Singapore for helpful discussion. This work was supported in part by the National Natural Science Foundation of China (61100124, 21106095, 61170239, and 61202168), the Grant of Elite Scholar Program of Tianjin University, the Grant of Introducing Talents to Tianjin Normal University (5RL123), the Grant of Introduction of One Thousand High-level Talents in Three Years in Tianjin.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Weizhi Nie
    • 1
  • Anan Liu
    • 1
    Email author
  • Zhongyang Wang
    • 1
  • Yuting Su
    • 1
  1. 1.The department of Electronics Information EngineeringTianjin UniversityTianjinChina

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