3D Object Recognition Using Convolutional Neural Networks with Transfer Learning Between Input Channels

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 302)

Abstract

RGB-D data is getting ever more interest from the research community as both cheap cameras appear in the market and the applications of this type of data become more common. A current trend in processing image data is the use of convolutional neural networks (CNNs) that have consistently beat competition in most benchmark data sets. In this paper, we investigate the possibility of transferring knowledge between CNNs when processing RGB-D data with the goal of both improving accuracy and reducing training time. We present experiments that show that our proposed approach can achieve both these goals.

Keywords

3D object recognition Transfer learning Convolutional neural networks 

Notes

Acknowledgments

This work was partially financed by FEDER funds through the “Programa Operacional Factores de Competitividade—COMPETE” and by Portuguese funds through “FCT—Fundação para a Ciência e a Tecnologia” in the framework of the project PTDC/EIA-EIA/119004/2010 and PEst-OE/EEI/LA0008/2013.

References

  1. 1.
    Pronobis, A., Mozos, O.M., Caputo, B., Jensfelt, P.: Multi-modal semantic place classification. I. J. Robotic Res. 29(2–3) (2010) 298–320.Google Scholar
  2. 2.
    Alexandre, L.A.: 3D descriptors for object and category recognition: a comparative evaluation. In: Workshop on Color-Depth Camera Fusion in Robotics at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vilamoura, Portugal (October 2012).Google Scholar
  3. 3.
    Bengio, Y.: Learning deep architectures for AI. Foundations and Trends in Machine Learning 2(1) (2009) 1–127.Google Scholar
  4. 4.
    Le Roux, N., Bengio, Y.: Deep belief networks are compact universal approximators. Neural computation 22(8) (2010) 2192–2207.MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural computation 18(7) (2006) 1527–1554.MathSciNetCrossRefMATHGoogle Scholar
  6. 6.
    Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.A.: Extracting and composing robust features with denoising autoencoders. In: Machine Learning, Proceedings of the Twenty-Fifth International Conference (ICML 2008), Helsinki, Finland, June 5–9, 2008. (2008) 1096–1103.Google Scholar
  7. 7.
    LeCun, Y., Bengio, Y.: Convolutional networks for images, speech, and time-series. In Arbib, M.A., ed.: The Handbook of Brain Theory and Neural Networks, MIT Press (1995).Google Scholar
  8. 8.
    Ciresan, D.C., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA (June 2012) 3642–3649.Google Scholar
  9. 9.
    Filipe, S., Alexandre, L.A.: From the human visual system to the computational models of visual attention: A survey. Artificial Intelligence Review (January 2013) 1–47.Google Scholar
  10. 10.
    Fukushima, K.: Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics 36(4) (1980) 193–202.CrossRefMATHGoogle Scholar
  11. 11.
    Socher, R., Huval, B., Bath, B., Manning, C., Ng, A.: Convolutional-recursive deep learning for 3d object classification. In Bartlett, P., Pereira, F., Burges, C., Bottou, L., Weinberger, K., eds.: Advances in Neural Information Processing Systems 25. (2012) 665–673.Google Scholar
  12. 12.
    Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering 22(10) (2010) 1345–1359.CrossRefGoogle Scholar
  13. 13.
    Ciresan, D.C., Meier, U., Schmidhuber, J.: Transfer learning for latin and chinese characters with deep neural networks. In: The 2012 International Joint Conference on Neural Networks, Brisbane, Australia (June 2012) 1301–1306.Google Scholar
  14. 14.
    Amaral, T., Sá, J., Silva, L., Alexandre, L.A., Santos, J.: Improving performance on problems with few labelled data by reusing stacked auto-encoders. In: submitted. (2014).Google Scholar
  15. 15.
    Lai, K., Bo, L., Ren, X., Fox, D.: A Large-Scale hierarchical Multi-View RGB-D object dataset. In: Proc. of the IEEE International Conference on Robotics & Automation (ICRA). (2011).Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Informatics and Instituto de TelecomunicaçõesUniversity of Beira InteriorCovilhãPortugal

Personalised recommendations