Abstract
The essence of image classification task is to extract high-level semantic content features of images. The traditional data enhancement methods based on convolutional neural network (CNN) are translation, rotation, clipping, noise adding, etc. These methods have not changed the content and style of image data. This paper proposes a fast style migration data enhancement method, which can quickly apply the style art of one image to another image without changing the high-level semantic content characteristics of the image. Through the experimental comparison, it is found that the method of fast style migration data enhancement proposed here can further improve the accuracy of the model compared with the traditional data.
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References
Guo, Y., Rothfus, T.A., Ashour, A.S., Si, L., Chunlai, D., Ting, T.‐F.: Varied channels region proposal and classification network for wildlife image classification under complex environment. IET Image Process. 14(4), 585–591 (2020). https://doi.org/10.1049/iet-ipr.2019.1042
Seo, S., Do, W.-J., Luu, H.M., Kim, K.H., Choi, S.H., Park, S.-H.: Artificial neural network for Slice Encoding for Metal Artifact Correction (SEMAC) MRI. Magn. Reson. Med. 84(1), 263–276 (2020). https://doi.org/10.1002/mrm.28126
An, T., et al.: Black tea withering moisture detection method based on convolution neural network confidence. Food Process Eng. 43(7),(2020). https://doi.org/10.1111/jfpe.13428
Qi, Y., Chen, J., Huo, Y., Li, F.: Hyperspectral image classification algorithm based on multiscale convolutional neural network. Infrared Technol. 42(9), 855–862 (2020). https://doi.org/10.3724/SP.J.7102910261
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)
Helmrich, I.R.A.R., van Klaveren, D., Steyerberg, E.W.: Research Note: prognostic model research: overfitting, validation and application. J. Physiother. 65(4), 243–245 (2019). https://doi.org/10.1016/j.jphys.2019.08.009
Gatys, L., Ecker, A., Bethge, M.: A neural algorithm of artistic style. J. Vis. 16(12), 326 (2016). https://doi.org/10.1167/16.12.326
Zhao, X., Zhao, X.-M.: Deep learning of brain magnetic resonance images: a brief review. Methods 192, 131–140 (2021). https://doi.org/10.1016/j.ymeth.2020.09.007
Zhang, Z., Tong Zhou, Y., Zhang, Y.P.: Attention-based deep residual learning network for entity relation extraction in Chinese EMRs. BMC Med. Inf. Decis. Making 19(S2) (2019). https://doi.org/10.1186/s12911-019-0769-0
Chen, J., Xiang, Y.: Summarization of research on gradient instability in deep neural network training. 29(07), 2071–2091 (2018)
Yuexiu, G., Wei, Y., Qi, L., Wang, Y.: Summary of residual network research. Comput. Appl. Res. 37(05), 1292–1297 (2020)
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. J. Mach. Learn. Res. 9, 249–256 (2010)
Tomoyuki, O., Kouyu, S., Naohiko, K.: Proposal and evaluation of pavement deterioration prediction method by recurrent neural network. Int. J. Adv. Res. Eng. 3(4), 16 (2017)
Castillioni, K., Wilcox, K., Jiang, L., Luo, Y., Jung, C.G., Souza, L.: Drought mildly reduces plant dominance in a temperate prairie ecosystem across years. Ecol. Evol. 10(13), 6702–6713 (2020). https://doi.org/10.1002/ece3.6400
Chunshui, C., Yongzhen, H., Yi, Y., et al.: Feedback convolutional neural network for visual localization and segmentation. IEEE Trans. Patt. Anal. Mach. Intell. 41(7), 1627–1640 (2019)
Acknowledgements
This work has been partially supported by “Heilongjiang Science Foundation Project (LH2021F052)” and “2020 scientific research project of basic scientific research expenses of provincial colleges and universities in Heilongjiang Province (2020-KYYWF-0684)”.
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Zheng, S., Wu, J., Liu, F., Pan, J., Qiao, Z. (2022). Research on Image Binary Classification Based on Fast Style Transfer Data Enhancement. In: Jiang, X. (eds) Machine Learning and Intelligent Communications. MLICOM 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 438. Springer, Cham. https://doi.org/10.1007/978-3-031-04409-0_8
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