Object Classification in Images of Neoclassical Furniture Using Deep Learning

  • Bernhard BermeitingerEmail author
  • André Freitas
  • Simon Donig
  • Siegfried Handschuh
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 482)


This short paper outlines research results on object classification in images of Neoclassical furniture. The motivation was to provide an object recognition framework which is able to support the alignment of furniture images with a symbolic level model. A data-driven bottom-up research routine in the Neoclassica research framework is the main use-case. This research framework is described more extensively by Donig et al. [2]. It strives to deliver tools for analyzing the spread of aesthetic forms which are considered as a cultural transfer process.


Deep learning Convolutional Neural Network Neoclassicism Object classification Furniture Art History Digital humanities 


  1. 1.
    Dieleman, S., Schlüter, J., Raffel, C., Olson, E., Sønderby, S.K., Nouri, D., Maturana, D., Thoma, M., Battenberg, E., Kelly, J., Fauw, J.D., Heilman, M., Diogo149, McFee, B., Weideman, H., Takacsg84, Peterderivaz, Jon, Instagibbs, Rasul, D.K., CongLiu, Britefury, Degrave, J.: Lasagne: First release, August 2015.
  2. 2.
    Donig, S., Christoforaki, M., Handschuh, S.: Neoclassica - a multilingual domain ontology. In: Bozic, M.-G., Debruyne, O’Sullivan (eds.) 2nd IFIP International Workshop on Computational History and Data-Driven Humanities (2016)Google Scholar
  3. 3.
    Prown, J.D.: Style as evidence. Winterthur Portfolio 15(3), 197–210 (1980)CrossRefGoogle Scholar
  4. 4.
    Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Shamir, L.: Computer analysis reveals similarities between the artistic styles of van gogh and pollock. Leonardo 45(2), 149–154 (2012)CrossRefGoogle Scholar
  6. 6.
    Shamir, L., Tarakhovsky, J.A.: Computer analysis of art. J. Comput. Cult. Heritage 5(2), 1–11 (2012)CrossRefGoogle Scholar
  7. 7.
    Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition. CoRR abs/1409.1 (2014)Google Scholar
  8. 8.
    Theano Development Team: Theano: A Python framework for fast computation of mathematical expressions. arXiv e-prints abs/1605.02688 (2016).

Copyright information

© IFIP International Federation for Information Processing 2016

Authors and Affiliations

  • Bernhard Bermeitinger
    • 1
    Email author
  • André Freitas
    • 1
  • Simon Donig
    • 1
  • Siegfried Handschuh
    • 1
  1. 1.Universität PassauPassauGermany

Personalised recommendations