Abstract
In this paper, we propose a novel data augmentation method of fine-grained image classification named target cropping. Previous work has demonstrated the effectiveness of data augmentation through simple technique, such as random cropping, image rotating and image flipping. But for fine-grained classification, due to its inter-class similarity and intra-class differences, traditional random cropping does not pay much attention to the discriminative regions and even may crop out the regions that have a critical impact on the classification results. To solve this problem, we propose target cropping which uses class activation maps to locate discriminative region. Compared with random cropping, our method significantly improves classification accuracy for all the tested datasets. For example, classification accuracy is improved from 71.1% to 73.9% for CUB200-2011 dataset with VGG-16 and from 77.2% to 79.0% in the FGVC-Aircraft dataset. It is a significant improvement in fine-grained image classification field.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bergamo, A., Bazzani, L., Anguelov, D., Torresani, L.: Self-taught object localization with deep networks (2016)
Cinbis, R.G., Verbeek, J., Schmid, C.: Weakly supervised object localization with multi-fold multiple instance learning (2017)
Fadaee, M., Bisazza, A., Monz, C.: Data augmentation for low-resource neural machine translation (2017)
Fu, J., Zheng, H., Tao, M.: Look closer to see better: recurrent attention convolutional neural network for fine-grained image recognition. In: Computer Vision & Pattern Recognition (2017)
Inoue, H.: Data augmentation by pairing samples for images classification (2018)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Lin, T.Y., Roychowdhury, A., Maji, S.: Bilinear CNN models for fine-grained visual recognition (2015)
Maji, S., Rahtu, E., Kannala, J., Blaschko, M., Vedaldi, A.: Fine-grained visual classification of aircraft. HAL - INRIA (2013)
Oquab, M., Bottou, L., Laptev, I., Sivic, J.: Learning and transferring mid-level image representations using convolutional neural networks. In: Computer Vision & Pattern Recognition (2014)
Perez, L., Wang, J.: The effectiveness of data augmentation in image classification using deep learning (2017)
Shu, X., Tang, J., Qi, G.J., Li, Z., Jiang, Y.G., Yan, S.: Image classification with tailored fine-grained dictionaries. IEEE Trans. Circ. Syst. Video Technol. 28(2), 454–467 (2018)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Computer Science (2014)
Wah, C., et al.: The caltech-ucsd birds-200-2011 dataset (2011)
Yu, Z., Wei, X.S., Wu, J., Cai, J., Lu, J., Nguyen, V.A., Do, M.N.: Weakly supervised fine-grained categorization with part-based image representation. IEEE Trans. Image Process. 25(4), 1713–1725 (2016)
Zhang, X., Xiong, H., Zhou, W., Lin, W., Qi, T.: Picking deep filter responses for fine-grained image recognition. In: Computer Vision & Pattern Recognition (2016)
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization (2015)
Zou, C., Luo, Y., Xiaolong, X.U.: Fine-grained image classification method based on multi-feature combination. J. Comput. Appl. (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Lu, J., Liao, M. (2020). Target Cropping: A New Data Augmentation Method of Fine-Grained Image Classification. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1074. Springer, Cham. https://doi.org/10.1007/978-3-030-32456-8_37
Download citation
DOI: https://doi.org/10.1007/978-3-030-32456-8_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32455-1
Online ISBN: 978-3-030-32456-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)