Deep Multibranch Neural Network for Painting Categorization

  • Simone Bianco
  • Davide Mazzini
  • Raimondo Schettini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10484)


Coarse features, such as scene composition and subject together with fine details, such as strokes and line styles, are useful clues for painter and style categorization. In this work, to automatically predict painting’s artist and style, we propose a novel deep multibranch neural network, where the different branches process the input image at different scales to jointly model the fine and coarse features of the painting. Experiments for both artist and style classification tasks are performed on the challenging Painting-91 dataset, that includes 91 different painters and 13 diverse painting styles. Our method outperforms the best method in the state of the art by 14.0% and 9.6% on artist and style classification respectively.


Painting categorization Painting style classification Painter recognition Deep convolutional neural network Multiresolution 


  1. 1.
    Anwer, R.M., Khan, F.S., van de Weijer, J., Laaksonen, J.: Combining holistic and part-based deep representations for computational painting categorization. In: Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval, pp. 339–342. ACM (2016)Google Scholar
  2. 2.
    Bianco, S., Buzzelli, M., Mazzini, D., Schettini, R.: Deep learning for logo recognition. Neurocomputing (2017).
  3. 3.
    Bianco, S., Mazzini, D., Pau, D., Schettini, R.: Local detectors and compact descriptors for visual search: a quantitative comparison. Digit. Sig. Proc. 44, 1–13 (2015)CrossRefGoogle Scholar
  4. 4.
    Carneiro, G., da Silva, N.P., Del Bue, A., Costeira, J.P.: Artistic image classification: an analysis on the PRINTART database. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7575, pp. 143–157. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-33765-9_11 CrossRefGoogle Scholar
  5. 5.
    Cimpoi, M., Maji, S., Vedaldi, A.: Deep filter banks for texture recognition and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3828–3836 (2015)Google Scholar
  6. 6.
    Gong, Y., Wang, L., Guo, R., Lazebnik, S.: Multi-scale orderless pooling of deep convolutional activation features. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8695, pp. 392–407. Springer, Cham (2014). doi: 10.1007/978-3-319-10584-0_26 Google Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    Hentschel, C., Wiradarma, T.P., Sack, H.: Fine tuning CNNS with scarce training data–adapting imagenet to art epoch classification. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 3693–3697. IEEE (2016)Google Scholar
  9. 9.
    Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on Machine Learning, pp. 448–456 (2015)Google Scholar
  10. 10.
    Khan, F.S., Beigpour, S., Van de Weijer, J., Felsberg, M.: Painting-91: a large scale database for computational painting categorization. Mach. Vis. Appl. 25(6), 1385–1397 (2014)CrossRefGoogle Scholar
  11. 11.
    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
  12. 12.
    Mensink, T., Van Gemert, J.: The rijksmuseum challenge: museum-centered visual recognition. In: Proceedings of International Conference on Multimedia Retrieval, p. 451. ACM (2014)Google Scholar
  13. 13.
    Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML 2010), pp. 807–814 (2010)Google Scholar
  14. 14.
    Peng, K.C., Chen, T.: Cross-layer features in convolutional neural networks for generic classification tasks. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 3057–3061. IEEE (2015)Google Scholar
  15. 15.
    Peng, K.C., Chen, T.: A framework of extracting multi-scale features using multiple convolutional neural networks. In: 2015 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6. IEEE (2015)Google Scholar
  16. 16.
    Sharif 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
  17. 17.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedings of the International Conference on Learning Representations (ICLR) (2015)Google Scholar
  18. 18.
    Tan, W.R., Chan, C.S., Aguirre, H.E., Tanaka, K.: Ceci n’est pas une pipe: A deep convolutional network for fine-art paintings classification. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 3703–3707. IEEE (2016)Google Scholar
  19. 19.
    Westlake, N., Cai, H., Hall, P.: Detecting people in artwork with CNNs. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9913, pp. 825–841. Springer, Cham (2016). doi: 10.1007/978-3-319-46604-0_57 CrossRefGoogle Scholar
  20. 20.
    Widjaja, I., Leow, W.K., Wu, F.C.: Identifying painters from color profiles of skin patches in painting images. In: Proceedings of 2003 International Conference on Image Processing, ICIP 2003, vol. 1, pp. I–845. IEEE (2003)Google Scholar
  21. 21.
    Zhao, L., Wang, K., Do, B.: Sherlocknet: exploring 400 years of western book illustrations with convolutional neural networks. Technical report, Stanford University (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Simone Bianco
    • 1
  • Davide Mazzini
    • 1
  • Raimondo Schettini
    • 1
  1. 1.Dipartimento di Informatica, Sistemistica e ComunicazioneUniversità degli Studi di Milano-BicoccaMilanItaly

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