Classification of Artistic Styles Using Binarized Features Derived from a Deep Neural Network

  • Yaniv BarEmail author
  • Noga Levy
  • Lior Wolf
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8925)


With the vast expansion of digital contemporary painting collections, automatic theme stylization has grown in demand in both academic and commercial fields. The recent interest in deep neural networks has provided powerful visual features that achieve state-of-the-art results in various visual classification tasks. In this work, we examine the perceptiveness of these features in identifying artistic styles in paintings, and suggest a compact binary representation of the paintings. Combined with the PiCoDes descriptors, these features show excellent classification results on a large scale collection of paintings.


Local Binary Pattern Convolutional Neural Network Deep Neural Network Feature Fusion Binarized Feature 
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.


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  1. 1.
    Arora, R.S.: Towards automated classification of fine-art painting style: A comparative study. Ph.D. thesis, Rutgers University-Graduate School-New Brunswick (2012)Google Scholar
  2. 2.
    Beckett, W., Wright, P.: The story of painting. Dorling Kindersley London (1994)Google Scholar
  3. 3.
    Ben-Shalom, I., Levy, N., Wolf, L., Dershowitz, N., Ben-Shalom, A., Shweka, R., Choueka, Y., Hazan, T., Bar, Y.: Congruency-based reranking. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2014)Google Scholar
  4. 4.
    Bergamo, A., Torresani, L.: Meta-class features for large-scale object categorization on a budget. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3085–3092. IEEE (2012)Google Scholar
  5. 5.
    Bergamo, A., Torresani, L., Fitzgibbon, A.W.: Picodes: Learning a compact code for novel-category recognition. In: Advances in Neural Information Processing Systems, pp. 2088–2096 (2011)Google Scholar
  6. 6.
    Bosch, A., Zisserman, A., Munoz, X.: Image classification using rois and multiple kernel learning. International Journal of Computer Vision 2008, 1–25 (2008)Google Scholar
  7. 7.
    Csurka, G., Dance, C., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. Workshop on Statistical Learning in Computer Vision, ECCV 1, 1–2 (2004)Google Scholar
  8. 8.
    Deac, A.I., van der Lubbe, J., Backer, E.: Feature Selection for Paintings Classification by Optimal Tree Pruning. In: Gunsel, B., Jain, A.K., Tekalp, A.M., Sankur, B. (eds.) MRCS 2006. LNCS, vol. 4105, pp. 354–361. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  9. 9.
    Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 248–255. IEEE (2009)Google Scholar
  10. 10.
    Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., Darrell, T.: Decaf: A deep convolutional activation feature for generic visual recognition. arXiv preprint arXiv:1310.1531 (2013)
  11. 11.
    Douze, M., Jégou, H., Sandhawalia, H., Amsaleg, L., Schmid, C.: Evaluation of gist descriptors for web-scale image search. In: Proceedings of the ACM International Conference on Image and Video Retrieval. p. 19. ACM (2009)Google Scholar
  12. 12.
    Ivanova, K., Stanchev, P., Velikova, E., Vanhoof, K., Depaire, B., Kannan, R., Mitov, I., Markov, K.: Features for Art Painting Classification Based on Vector Quantization of MPEG-7 Descriptors. In: Kannan, R., Andres, F. (eds.) ICDEM 2010. LNCS, vol. 6411, pp. 146–153. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  13. 13.
    Jin, H., Liu, Q., Lu, H., Tong, X.: Face detection using improved lbp under bayesian framework. In: 2004 IEEE First Symposium on Multi-Agent Security and Survivability, pp. 306–309. IEEE (2004)Google Scholar
  14. 14.
    Johnson, C.R., Hendriks, E., Berezhnoy, I.J., Brevdo, E., Hughes, S.M., Daubechies, I., Li, J., Postma, E., Wang, J.Z.: Image processing for artist identification. IEEE Signal Processing Magazine 25(4), 37–48 (2008)CrossRefGoogle Scholar
  15. 15.
    Jou, J., Agrawal, S.: Artist identification for renaissance paintingsGoogle Scholar
  16. 16.
    Karayev, S., Hertzmann, A., Winnemoeller, H., Agarwala, A., Darrell, T.: Recognizing image style. arXiv preprint arXiv:1311.3715 (2013)
  17. 17.
    Keren, D.: Painter identification using local features and naive bayes. In: Proceedings of the 16th International Conference on Pattern Recognition, vol. 2, pp. 474–477. IEEE (2002)Google Scholar
  18. 18.
    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
  19. 19.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2169–2178. IEEE (2006)Google Scholar
  20. 20.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  21. 21.
    Mishory, A.: Art history: an introduction. Open University of Israel (2000)Google Scholar
  22. 22.
    Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recognition 29(1), 51–59 (1996)CrossRefGoogle Scholar
  23. 23.
    Oliva, A., Torralba, A.: Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision 42(3), 145–175 (2001)CrossRefzbMATHGoogle Scholar
  24. 24.
    Oquab, M., Bottou, L., Laptev, I., Sivic, J., et al.: Learning and transferring mid-level image representations using convolutional neural networks (2013)Google Scholar
  25. 25.
    Parker, J.R.: Algorithms for image processing and computer vision. John Wiley & Sons (2010)Google Scholar
  26. 26.
    Perronnin, F., Dance, C.: Fisher kernels on visual vocabularies for image categorization. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8. IEEE (2007)Google Scholar
  27. 27.
    Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229 (2013)
  28. 28.
    Sharif Razavian, A., Azizpour, H., Sullivan, J., Carlsson, S.: Cnn features off-the-shelf: an astounding baseline for recognition. arXiv preprint arXiv:1403.6382 (2014)
  29. 29.
    Shechtman, E., Irani, M.: Matching local self-similarities across images and videos. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8. IEEE (2007)Google Scholar
  30. 30.
    Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: Deepface: Closing the gap to human-level performance in face verification. In: IEEE CVPR (2014)Google Scholar
  31. 31.
    Torresani, L., Szummer, M., Fitzgibbon, A.: Efficient Object Category Recognition Using Classemes. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 776–789. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  32. 32.
    Zujovic, J., Gandy, L., Friedman, S., Pardo, B., Pappas, T.N.: Classifying paintings by artistic genre: An analysis of features & classifiers. In: IEEE International Workshop on Multimedia Signal Processing, MMSP 2009, pp. 1–5. IEEE (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.The Blavatnik School of Computer ScienceTel Aviv UniversityTel Aviv-YafoIsrael

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