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    A Novel Image Tag Completion Method Based on Convolutional Neural Transformation

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    Type
    Conference Paper
    Authors
    Geng, Yanyan
    Zhang, Guohui
    Li, Weizhi
    Gu, Yi
    Liang, Ru-Ze cc
    Liang, Gaoyuan
    Wang, Jingbin
    Wu, Yanbin
    Patil, Nitin
    Wang, Jing-Yan
    KAUST Department
    Material Science and Engineering Program
    Physical Science and Engineering (PSE) Division
    Date
    2017-10-25
    Online Publication Date
    2017-10-25
    Print Publication Date
    2017
    Permanent link to this record
    http://hdl.handle.net/10754/626775
    
    Metadata
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    Abstract
    In the problems of image retrieval and annotation, complete textual tag lists of images play critical roles. However, in real-world applications, the image tags are usually incomplete, thus it is important to learn the complete tags for images. In this paper, we study the problem of image tag complete and proposed a novel method for this problem based on a popular image representation method, convolutional neural network (CNN). The method estimates the complete tags from the convolutional filtering outputs of images based on a linear predictor. The CNN parameters, linear predictor, and the complete tags are learned jointly by our method. We build a minimization problem to encourage the consistency between the complete tags and the available incomplete tags, reduce the estimation error, and reduce the model complexity. An iterative algorithm is developed to solve the minimization problem. Experiments over benchmark image data sets show its effectiveness.
    Citation
    Geng Y, Zhang G, Li W, Gu Y, Liang R-Z, et al. (2017) A Novel Image Tag Completion Method Based on Convolutional Neural Transformation. Lecture Notes in Computer Science: 539–546. Available: http://dx.doi.org/10.1007/978-3-319-68612-7_61.
    Publisher
    Springer Nature
    Journal
    Artificial Neural Networks and Machine Learning – ICANN 2017
    Conference/Event name
    26th International Conference on Artificial Neural Networks, ICANN 2017
    DOI
    10.1007/978-3-319-68612-7_61
    Additional Links
    https://link.springer.com/chapter/10.1007%2F978-3-319-68612-7_61
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-319-68612-7_61
    Scopus Count
    Collections
    Conference Papers; Physical Science and Engineering (PSE) Division; Material Science and Engineering Program

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