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CNN-feature based automatic image annotation method

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Abstract

Automatic image annotation(AIA) methods are considered as a kind of efficient schemes to solve the problem of semantic-gap between the original images and their semantic information. However, traditional annotation models work well only with finely crafted manual features. To address this problem, we combined the CNN feature of an image into our proposed model which we referred as SEM by using a famous CNN model-AlexNet. We extracted a CNN feature by removing its final layer and it is proved to be useful in our SEM model. Additionally, based on the experience of the traditional KNN models, we propose a model to address the problem of simultaneously addressing the image tag refinement and assignment while maintaining the simplicity of the KNN model. The proposed model divides the images which have similar features into a semantic neighbor group. Moreover, utilizing a self-defined Bayesian-based model, we distribute the tags which belong to the neighbor group to the test images according to the distance between the test image and the neighbors. At last, the experiments are performed on three typical image datasets corel5k, espGame and laprtc12, which verify the effectiveness of the proposed model.

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Acknowledgments

This research is partially supported by Natural Science Foundation of China (Grant No.61602353) and the Fundamental Research Funds for the Central Universities (WUT:2017IVA053, WUT:2017IVB028 and WUT:2017YB028).

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Authors and Affiliations

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

    Yanchun Ma, Yongjian Liu, Qing Xie & Lin Li

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  1. Yanchun Ma
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  2. Yongjian Liu
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  3. Qing Xie
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  4. Lin Li
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Correspondence to Qing Xie.

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Ma, Y., Liu, Y., Xie, Q. et al. CNN-feature based automatic image annotation method. Multimed Tools Appl 78, 3767–3780 (2019). https://doi.org/10.1007/s11042-018-6038-x

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