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A weighted KNN-based automatic image annotation method

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Abstract

Automatic image annotation becomes a hot research area because of its efficiency on shrinking the semantic gap between images and their semantic meanings. We present a model referred as weight-KNN which firstly introduces the CNN feature to address the problem that traditional models only work well with well-designed manual feature representations. Additionally, in order to employ the simplicity and generality of the KNN-based model for annotation, the proposed model incorporates a multi-label linear discriminant approach to compute the weighting which improves the accuracy in the subsequent procedures of distance calculation. Moreover, we take the advantage of the KNN-based model to acquire the test image’s k-nearest neighbors in each label category and get the prediction of the image according to the contribution of its neighbors. At last, the experiments are performed on three typical image data sets, corel 5k, esp game and laprtc12, which verify the effectiveness of the proposed model.

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Acknowledgements

This work is partially supported by Natural Science Foundation of China (Grant No. 61602353), Natural Science Foundation of Hubei Province (Grant No. 2017CFB505) and the Fundamental Research Funds for the Central Universities (WUT:2017YB028).

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Authors and Affiliations

  1. School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China

    Yanchun Ma, Qing Xie, Yongjian Liu & Shengwu Xiong

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  1. Yanchun Ma
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  2. Qing Xie
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  3. Yongjian Liu
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  4. Shengwu Xiong
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Correspondence to Qing Xie.

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Ma, Y., Xie, Q., Liu, Y. et al. A weighted KNN-based automatic image annotation method. Neural Comput & Applic 32, 6559–6570 (2020). https://doi.org/10.1007/s00521-019-04114-y

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