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Image tag recommendation based on friendships

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Abstract

The tagging systems have been studied by many researchers in the past decade. Tagging methods have been widely used on the web for searching and recommending images. Social tags are the keywords annotated by users to the images, which contains the information for searching and classifying the images. Tag recommendation system allows mitigating the individual preferences to annotate and recommender images. However, irrelevant and noise tags are frequently included in tags. In this paper, we propose image tag recommendation based on the friends’ relationships in social network (TRboFS) to recommender tags for a new image, both the tags assigned to the favorite images and the friendships of the users who upload the image are employed to predict the tags of the images. Empirical analyses on real datasets show that the proposed approach achieves superior performance to existing approaches.

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Acknowledgments

The work is partially supported by the National Natural Science Foundation of China (No.61375121), the Research Projects of Natural Science and Teaching Reformation and Top-notch Academic Programs for Jiangsu Higher Education Institutions (Nos.14KJD520003, 2015JSJG163, PPZY2015B140), the Scientific Research Foundation of Jinling Institute of Technology (No.jit-rcyj-201505), and sponsored by the Funds for Nanjing Creative Team of Swarm Computing & Smart Software Led by Prof. S. Su (Corresponding author).

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Authors and Affiliations

  1. School of Computer Engineering, Jinling Institute of Technology, Nanjing, 211169, China

    Guo Haifeng & Su Shoubao

  2. School of Computer & Information Engineering, Hohai University, Nanjing, 210098, China

    Guo Haifeng

  3. School of Technology, Nanjing Audit University, Nanjing, 211815, China

    Sun Zhoubao

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  1. Guo Haifeng
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  2. Su Shoubao
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  3. Sun Zhoubao
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Correspondence to Su Shoubao.

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Haifeng, G., Shoubao, S. & Zhoubao, S. Image tag recommendation based on friendships. Multimed Tools Appl 76, 14581–14597 (2017). https://doi.org/10.1007/s11042-016-3802-7

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  • DOI: https://doi.org/10.1007/s11042-016-3802-7

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