Abstract
Traditionally, it is a time-consuming work for experts to accomplish pollen grains classification. With the popularity of deep Convolutional Neural Network (CNN) in computer vision, many automatic pollen grains classification methods based on CNN have been proposed in recent years. However, The CNN they used often focus on the most proniment area in the center of pollen grains and neglect the less discriminative local features in the surrounding of pollen grains. In order to alleviate this situation, we propose two data augmentation operations. Our experiment results on Pollen13K achieve a weighted F1 score of 97.26% and an accuracy of 97.29%.
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Battiato, S., Ortis, A., Trenta, F., Ascari, L., Politi, M., Siniscalco, C.: Detection and classification of pollen grain microscope images. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR Workshops 2020, Seattle, WA, USA, 14–19 June 2020, pp. 4220–4227. IEEE (2020)
Battiato, S., Ortis, A., Trenta, F., Ascari, L., Politi, M., Siniscalco, C.: Pollen13k: a large scale microscope pollen grain image dataset (2020)
Buters, J.T.M.: Pollen and spore monitoring in the world. Clin. Transl. Allergy 8(1), 9 (2018)
Daood, A., Ribeiro, E., Bush, M.: Pollen grain recognition using deep learning. In: Bebis, G., et al. (eds.) ISVC 2016. LNCS, vol. 10072, pp. 321–330. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-50835-1_30
DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017)
Gonçalve, A.B., et al.: Feature extraction and machine learning for the classification of Brazilian savannah pollen grains. Plos ONE 11, e0157044 (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)
Khanzhina, N., Putin, E., Filchenkov, A., Zamyatina, E.: Pollen grain recognition using convolutional neural network. In: ESANN (2018)
Lin, M., Chen, Q., Yan, S.: Network in network. arXiv preprint arXiv:1312.4400 (2013)
Nguyen, N.R., Donalson-Matasci, M., Shin, M.C.: Improving pollen classification with less training effort. In: IEEE Workshop on Applications of Computer Vision (WACV), pp. 421–426. IEEE (2013)
Sevillano, V., Aznarte, J.L.: Improving classification of pollen grain images of the polen23e dataset through three different applications of deep learning convolutional neural networks. PloS ONE 13(9), e0201807 (2018)
Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 60 (2019)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
Acknowledgement
The authors would like to thank Mingbo Hong for his constructive discussion on the proposed method. All correspondences should be directed to Q. Zhao at qjzhao@scu.edu.cn.
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Gui, P., Wang, R., Zhu, Z., Zhu, F., Zhao, Q. (2021). Improved Data Augmentation of Deep Convolutional Neural Network for Pollen Grains Classification. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12668. Springer, Cham. https://doi.org/10.1007/978-3-030-68793-9_36
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DOI: https://doi.org/10.1007/978-3-030-68793-9_36
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