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The image annotation algorithm using convolutional features from intermediate layer of deep learning

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Abstract

The automatic image annotation is an effective computer operation that predicts the annotation of an unknown image by automatically learning potential relationships between the semantic concept space and the visual feature space in the annotation image dataset. Usually, the auto-labeling image includes the processing: learning processing and labeling processing. Existing image annotation methods that employ convolutional features of deep learning methods have a number of limitations, including complex training and high space/time expenses associated with the image annotation procedure. Accordingly, this paper proposes an innovative method in which the visual features of the image are presented by the intermediate layer features of deep learning, while semantic concepts are represented by mean vectors of positive samples. Firstly, the convolutional result is directly output in the form of low-level visual features through the mid-level of the pre-trained deep learning model, with the image being represented by sparse coding. Secondly, the positive mean vector method is used to construct visual feature vectors for each text vocabulary item, so that a visual feature vector database is created. Finally, the visual feature vector similarity between the testing image and all text vocabulary is calculated, and the vocabulary with the largest similarity used for annotation. Experiments on the datasets demonstrate the effectiveness of the proposed method; in terms of F1 score, the proposed method’s performance on the Corel5k dataset and IAPR TC-12 dataset is superior to that of MBRM, JEC-AF, JEC-DF, and 2PKNN with end-to-end deep features.

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Acknowledgments

We are grateful to all students and teachers who participated in this study and all the colleagues working to realize this project.

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 61972056, 61772454, 61402053, 61981340416, the Natural Science Foundation of Hunan Province of China under Grant 2020JJ4623, the Scientific Research Fund of Hunan Provincial Education Department under Grant 17A007, 19C0028, 19B005, the Changsha Science and Technology Planning under Grant KQ1703018, KQ1706064, KQ1703018–01, KQ1703018–04, the Junior Faculty Development Program Project of Changsha University of Science and Technology under Grant 2019QJCZ011, the “Double First-class” International Cooperation and Development Scientific Research Project of Changsha University of Science and Technology under Grant 2019IC34, the Practical Innovation and Entrepreneurship Ability Improvement Plan for Professional Degree Postgraduate of Changsha University of Science and Technology under Grant SJCX202072, the Postgraduate Training Innovation Base Construction Project of Hunan Province under Grant 2019–248-51, 2020–172-48.

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Authors and Affiliations

  1. School of Computer and Communication Engineering & Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, Changsha, 410114, Hunan, China

    Yuantao Chen, Linwu Liu, Jiajun Tao & Xi Chen

  2. Hunan Institute of Scientific and Technical Information, Changsha, 411105, Hunan, China

    Runlong Xia & Jingbo Xie

  3. Department of Electronic Products, Hunan ZOOMLION Intelligent Technology Corporation Limited, Changsha, 410005, Hunan, China

    Qian Zhang & Kai Yang

  4. Electronics & Information School, Yangtze University, Jingzhou, 434023, China

    Jie Xiong

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  1. Yuantao Chen
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  2. Linwu Liu
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Correspondence to Yuantao Chen.

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Chen, Y., Liu, L., Tao, J. et al. The image annotation algorithm using convolutional features from intermediate layer of deep learning. Multimed Tools Appl 80, 4237–4261 (2021). https://doi.org/10.1007/s11042-020-09887-2

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