Abstract
Pollen classification is an important task in many fields, including allergology, archaeobotany and biodiversity conservation. However, the visual classification of pollen grains is a major challenge due to the difficulty in identifying the subtle variations between the sub-categories of objects. The pollen image analysis process is often time-consuming and require expert evaluations. Even simple tasks, such as image classification or segmentation requires significant efforts from experts in aerobiology. Hence, there is a strong need to develop automatic solutions for microscopy image analysis. These considerations underline the effort to study and develop new efficient algorithms. With the growing interest in Deep Learning (DL), much research efforts have been spent to the development of several approaches to accomplish this task. Hence, this study covers the application of effective Deep Learning methods in combination with Fine-Grained Visual Classification (FGVC) approaches, comparing them with other Deep Learning-based methods from the state-of-art. All experiments were conducted using the dataset Pollen13K, composed of more than 13,000 pollen objects subdivided in 4 classes. The results of experiments confirmed the effectiveness of our proposed pipeline that reached over 97% in terms of accuracy and F1-score.
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Notes
- 1.
more details are available on the dataset website: https://iplab.dmi.unict.it/pollengraindataset/dataset.
References
Battiato, S., et al.: Detection and classification of pollen grain microscope images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 980–981 (2020)
Battiato, S., et al.: Pollen13k: a large scale microscope pollen grain image dataset. In: 2020 IEEE International Conference on Image Processing (ICIP), pp. 2456–2460. IEEE (2020)
Battiato, S., et al.: Pollen grain classification challenge 2020. In: Del Bimbo, A. (ed.) ICPR 2021. LNCS, vol. 12668, pp. 469–479. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68793-9_34
Buda, M., et al.: A systematic study of the class imbalance problem in convolutional neural networks. Neural Netw. 106, 249–259 (2018)
Chen, Y., et al.: Destruction and construction learning for fine-grained image recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5157–5166 (2019)
Du, R., Chang, D., Bhunia, A.K., Xie, J., Ma, Z., Song, Y.-Z., Guo, J.: Fine-grained visual classification via progressive multi-granularity training of jigsaw patches. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12365, pp. 153–168. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58565-5_10
Fang, C., Hu, Y., Zhang, B., Doermann, D., et al.: The fusion of neural architecture search and destruction and construction learning. In: Del Bimbo, A. (ed.) ICPR 2021. LNCS, vol. 12668, pp. 480–489. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68793-9_35
Fang, J., et al.: Densely connected search space for more flexible neural architecture search. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10628–10637 (2020)
Gui, P., Wang, R., Zhu, Z., Zhu, F., Zhao, Q., et al.: Improved data augmentation of deep convolutional neural network for pollen grains classification. In: Del Bimbo, A. (ed.) ICPR 2021. LNCS, vol. 12668, pp. 490–500. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68793-9_36
He, K., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Kiefer, J., et al.: Stochastic estimation of the maximum of a regression function. Ann. Math. Stat. 23(3), 462–466 (1952)
Kim, I., et al.: Learning loss for test-time augmentation. arXiv preprint arXiv:2010.11422 (2020)
Krizhevsky, A., et al.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25, 1097–1105 (2012)
Paszke, A., et al.: Automatic differentiation in pytorch (2017)
Simard, P.Y., LeCun, Y.A., Denker, J.S., Victorri, B.: Transformation invariance in pattern recognition – tangent distance and tangent propagation. In: Montavon, G., Orr, G.B., Müller, K.-R. (eds.) Neural Networks: Tricks of the Trade. LNCS, vol. 7700, pp. 235–269. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35289-8_17
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Tan, M., Le, Q.: Efficientnet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105–6114. PMLR (2019)
Wang, F., et al.: Residual attention network for image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3156–3164 (2017)
Wei, C., et al.: Iterative reorganization with weak spatial constraints: solving arbitrary jigsaw puzzles for unsupervised representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1910–1919 (2019)
Yun, S., et al.: Cutmix: regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6023–6032 (2019)
Zhang, H., et al.: Mixup: beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)
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Trenta, F., Ortis, A., Battiato, S. (2021). Fine-Grained Image Classification for Pollen Grain Microscope Images. In: Tsapatsoulis, N., Panayides, A., Theocharides, T., Lanitis, A., Pattichis, C., Vento, M. (eds) Computer Analysis of Images and Patterns. CAIP 2021. Lecture Notes in Computer Science(), vol 13052. Springer, Cham. https://doi.org/10.1007/978-3-030-89128-2_33
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