The Impact of Padding on Image Classification by Using Pre-trained Convolutional Neural Networks

  • Hongxiang Tang
  • Alessandro OrtisEmail author
  • Sebastiano Battiato
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11752)


The work presented in this paper aims to investigate the effect of pre-processing on image classification by using CNN pre-trained models. By considering how different quality factors of the input images affect the performances of a CNN based classifier, we propose a pre-processing pipeline (i.e., padding) that is able to improve the classification of the model on challenging images. The presented study allows to improve the performances by only acting on the input images, instead of re-training the model or augmenting the number of CNN’s parameters. This finds very practical applications, since such model adaptation requires high amounts of labelled data and computational costs.


Image preprocessing Padding Convolutional Neural network 


  1. 1.
    Battiato, S., Farinella, G.M., Puglisi, G., Ravi, D.: Saliency-based selection of gradient vector flow paths for content aware image resizing. IEEE Trans. Image Process. 23(5), 2081–2095 (2014)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Buslaev, A., Parinov, A., Khvedchenya, E., Iglovikov, V.I., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. arXiv preprint arXiv:1809.06839 (2018)
  3. 3.
    Chevalier, M., Thome, N., Cord, M., Fournier, J., Henaff, G., Dusch, E.: Low resolution convolutional neural network for automatic target recognition. In: 7th International Symposium on Optronics in Defence and Security (2016)Google Scholar
  4. 4.
    Dodge, S., Karam, L.: Understanding how image quality affects deep neural networks, pp. 1–6 (2016)Google Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    Öztürk, Ş., Akdemir, B.: Effects of histopathological image pre-processing on convolutional neural networks. Procedia Comput. Sci. 132, 396–403 (2018)CrossRefGoogle Scholar
  7. 7.
    Pal, K.K., Sudeep, K.: Preprocessing for image classification by convolutional neural networks. In: IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), pp. 1778–1781. IEEE (2016)Google Scholar
  8. 8.
    Peng, X., Hoffman, J., Yu, S.X., Saenko, K.: Fine-to-coarse knowledge transfer for low-res image classification. arXiv preprint arXiv:1605.06695 (2016)
  9. 9.
    Russakovsky, O., et al.: ImageNetLarge scale visual recognition challenge. Int. J. Comput. Vis. (IJCV) 115(3), 211–252 (2015). Scholar
  10. 10.
    Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Mathematics and Computer ScienceUniversity of CataniaCataniaItaly

Personalised recommendations