Artistic Photo Filtering Recognition Using CNNs

  • Simone Bianco
  • Claudio Cusano
  • Raimondo Schettini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10213)


In this paper we propose an approach based on deep Convolutional Neural Networks (CNNs) to recognize artistic photo filters applied to images. A total of 22 types of Instagram-like filters is considered. Different CNN architectures taken from the image recognition literature are compared on a dataset of more than 0.46 M images from the Places-205 dataset. Experimental results show that not only it is possible to reliably determine whether or not one of these filters has been applied, but also which one. Differently from other tasks, where the fine-tuning of a CNN trained on a different problem is usually good enough, here the fine-tuned AlexNet obtains an accuracy of only 67.5%. We show, instead, that an accuracy of about 99.0% can be obtained by training a CNN from scratch for this specific problem.


Confusion Matrix Convolutional Neural Network Convolutional Layer Deep Convolutional Neural Network Computer Vision Task 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Bengio, Y.: Deep learning of representations for unsupervised and transfer learning. In: Unsupervised and Transfer Learning Challenges in Machine Learning, vol. 7, p. 19 (2012)Google Scholar
  2. 2.
    Bianco, S.: Reflectance spectra recovery from tristimulus values by adaptive estimation with metameric shape correction. J. Opt. Soc. Am. A 27(8), 1868–1877 (2010)CrossRefGoogle Scholar
  3. 3.
    Bianco, S., Ciocca, G., Marini, F., Schettini, R.: Image quality assessment by preprocessing and full reference model combination. In: IS&T/SPIE Electronic Imaging, p. 72420O. International Society for Optics and Photonics (2009)Google Scholar
  4. 4.
    Bianco, S., Ciocca, G., Napoletano, P., Schettini, R.: An interactive tool for manual, semi-automatic and automatic video annotation. Comput. Vis. Image Underst. 131, 88–99 (2015)CrossRefGoogle Scholar
  5. 5.
    Bianco, S., Cusano, C., Schettini, R.: Color constancy using CNNs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 81–89 (2015)Google Scholar
  6. 6.
    Bianco, S., Cusano, C., Schettini, R.: Single and multiple illuminant estimation using convolutional neural networks. arXiv preprint (2015). arXiv:1508.00998
  7. 7.
    Bianco, S., Mazzini, D., Pau, D.P., Schettini, R.: Local detectors and compact descriptors for visual search: a quantitative comparison. Digital Signal Proc. 44, 1–13 (2015)CrossRefGoogle Scholar
  8. 8.
    Bianco, S., Schettini, R.: Computational color constancy. In: 2011 3rd European Workshop on, Visual Information Processing (EUVIP), pp. 1–7. IEEE (2011)Google Scholar
  9. 9.
    Chen, Y.H., Chao, T.H., Bai, S.Y., Lin, Y.L., Chen, W.C., Hsu, W.H.: Filter-invariant image classification on social media photos. In: Proceedings of the 23rd Annual ACM Conference on Multimedia Conference, pp. 855–858. ACM (2015)Google Scholar
  10. 10.
    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
  11. 11.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  12. 12.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  13. 13.
    Razavian, A., Azizpour, H., Sullivan, J., Carlsson, S.: CNN features off-the-shelf: an astounding baseline for recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 806–813 (2014)Google Scholar
  14. 14.
    Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211–252 (2015)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9 (2015)Google Scholar
  16. 16.
    Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks?. In: Advances in Neural Information Processing Systems, pp. 3320–3328 (2014)Google Scholar
  17. 17.
    Zhou, B., Lapedriza, A., Xiao, J., Torralba, A., Oliva, A.: Learning deep features for scene recognition using places database. In: Advances in Neural Information Processing Systems, pp. 487–495 (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Informatics, Systems and CommunicationUniversity of Milano-BicoccaMilanoItaly
  2. 2.Department of Electrical, Computer and Biomedical EngineeringUniversity of PaviaPaviaItaly

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