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International Symposium on Neural Networks

ISNN 2015: Advances in Neural Networks – ISNN 2015 pp 232–239Cite as

Image Tag Completion by Local Learning

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9377))

Abstract

The problem of tag completion is to learn the missing tags of an image. In this paper, we propose to learn a tag scoring vector for each image by local linear learning. A local linear function is used in the neighborhood of each image to predict the tag scoring vectors of its neighboring images. We construct a unified objective function for the learning of both tag scoring vectors and local linear function parameters. In the objective, we impose the learned tag scoring vectors to be consistent with the known associations to the tags of each image, and also minimize the prediction error of each local linear function, while reducing the complexity of each local function. The objective function is optimized by an alternate optimization strategy and gradient descent methods in an iterative algorithm. We compare the proposed algorithm against different state-of-the-art tag completion methods, and the results show its advantages.

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Author information

Authors and Affiliations

  1. National Time Service Center, Chinese Academy of Sciences, Xi’an, Shaanxi, 710600, China

    Jingyan Wang

  2. Graduate University of Chinese Academy of Sciences, Beijing, 100049, China

    Jingyan Wang

  3. Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, 215006, China

    Jingyan Wang

  4. Department of Mechanical Engineering and Mechanics, Lehigh University, Bethlehem, PA, 18015, US

    Yihua Zhou

  5. Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY, 14850, USA

    Haoxiang Wang

  6. College of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, 250014, China

    Xiaohong Yang & Feng Yang

  7. Electrical and Computer Engineering Department, The University of Texas at San Antonio, San Antonio, TX, 78249, USA

    Austin Peterson

Authors
  1. Jingyan Wang
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  2. Yihua Zhou
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  3. Haoxiang Wang
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  4. Xiaohong Yang
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  5. Feng Yang
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  6. Austin Peterson
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Corresponding author

Correspondence to Jingyan Wang .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Xiaolin Hu

  2. Fuzhou University, Fuzhou, China

    Yousheng Xia

  3. School of Information Science and Technology, Sun Yat-sen University, Guangzhou, China

    Yunong Zhang

  4. Chinese Academy of Sciences, Institute of Automation, Beijing, China

    Dongbin Zhao

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© 2015 Springer International Publishing Switzerland

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Wang, J., Zhou, Y., Wang, H., Yang, X., Yang, F., Peterson, A. (2015). Image Tag Completion by Local Learning. In: Hu, X., Xia, Y., Zhang, Y., Zhao, D. (eds) Advances in Neural Networks – ISNN 2015. ISNN 2015. Lecture Notes in Computer Science(), vol 9377. Springer, Cham. https://doi.org/10.1007/978-3-319-25393-0_26

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  • DOI: https://doi.org/10.1007/978-3-319-25393-0_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25392-3

  • Online ISBN: 978-3-319-25393-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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