Skip to main content
SpringerLink
Log in

Investigating a Dictionary-Based Non-negative Matrix Factorization in Superimposed Digits Classification Tasks

  • Conference paper
  • First Online:
Neural Information Processing (ICONIP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9949))

Included in the following conference series:

  • 3044 Accesses

Abstract

Human visual system can recognize superimposed graphical components with ease while sophisticated computer vision systems still struggle to recognize them. This may be attributed to the fact that the image recognition task is framed as a classification task where a classification model is commonly constructed from appearance features. Hence, superimposed components are perceived as a single image unit. It seems logical to approach the recognition of superimposed digits by employing an approach that supports construction/deconstruction of superimposed components. Here, we resort to a dictionary-based non-negative matrix factorization (NMF). The dictionary-based NMF factors a given superimposed digit matrix, V, into the combination of entries in the dictionary matrix W. The H matrix from \(V \approx WH\) can be interpreted as corresponding superimposed digits. This work investigates three different dictionary representations: pixels’ intensity, Fourier coefficients and activations from RBM hidden layers. The results show that (i) NMF can be employed as a classifier and (ii) dictionary-based NMF is capable of classifying superimposed digits with only a small set of dictionary entries derived from single digits.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The database is available from http://yann.lecun.com/exdb/mnist/, it consists of 60,000 training examples (roughly 6,000 different handwritten examples for each digit) and 10,000 testing examples (roughly 1,000 different handwritten examples for each digit).

References

  1. Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013). IEEE

    Article  Google Scholar 

  2. Bottou, L., Cortes, C., Denker, J.S., Drucker, H., Guyon, I., Jackel, L.D., LeCun, Y., Müller, U.A., Säckinger, E., Simard, P., Vapnik, V.: Comparison of classifier methods: a case study in handwritten digit recognition. In: Proceedings of the 12th IAPR International. Conference on Pattern Recognition, vol. 2, pp. 77–82. Conference B: Computer Vision & Image Processing (1994)

    Google Scholar 

  3. Ciresan, D.C., Meier, U., Masci, J., Gambardella, L.M., Schmidhuber, J.: Flexible, high performance convolutional neural networks for image classification. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence (IJCAI 2011), pp. 1237–1242 (2011)

    Google Scholar 

  4. Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)

    Article  Google Scholar 

  5. Phon-Amnuaisuk, S.: Applying non-negative matrix factorization to classify superimposed handwritten digits. Procedia Comput. Sci. 24(2013), 261–267 (2013)

    Article  Google Scholar 

  6. Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401, 788–791 (1999)

    Article  Google Scholar 

  7. Sohn, K., Lee, H.: Learning invariant representations with local transformations. In: Proceedings of the 29th International Conference on Machine Learning, ICML 2012, Edinburgh (2012)

    Google Scholar 

  8. Song, H.A., Kim, B.K., Xuan, T.L., Lee, S.Y.: Hierarchical feature extraction by multi-layer non-negative matrix factorization network for classification task. Neurocomputing 165, 63–74 (2015)

    Article  Google Scholar 

  9. Lee, S.Y., Mozer, M.C.: Robust recognition of noisy and superimposed patterns via selective attention. In: Proceedings of the International Conference on Neural Information Processing Systems (NIPS 1999), pp. 31–37 (1999)

    Google Scholar 

  10. Zhou, Z., Wagner, A., Mobahi, H., Wright, J., Ma., Y.: Face recognition with contiguous occlusion using markov random fields. In: Proceedings of the International Conference on Computer Vision (ICCV 2009), pp. 1050–1057. IEEE (2009)

    Google Scholar 

  11. Tang, Y., Salakhutdinov, R., Hinton, G.: Robust Boltzmann machines for recognition and denoising. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012), pp. 2264–2271. IEEE (2012)

    Google Scholar 

Download references

Acknowledgments

We wish to thank anonymous reviewers for their comments, which help improve this paper. We would like to thank the GSR office for their financial support given to this research.

Author information

Authors and Affiliations

  1. Media Informatics Special Interest Group, Centre for Innovative Engineering, Universiti Teknologi Brunei, Gadong, Brunei

    Somnuk Phon-Amnuaisuk

  2. Brain Science Research Center, Korea Advanced Institute of Science and Technology, Daejeon, Korea

    Soo-Young Lee

Authors
  1. Somnuk Phon-Amnuaisuk
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Soo-Young Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Somnuk Phon-Amnuaisuk .

Editor information

Editors and Affiliations

  1. The University of Tokyo , Tokyo, Japan

    Akira Hirose

  2. Kobe University , Kobe, Japan

    Seiichi Ozawa

  3. Okinawa Institute of Science and Technology Graduate University, Onna, Japan

    Kenji Doya

  4. Nara Institute of Science and Technology , Ikoma, Japan

    Kazushi Ikeda

  5. Kyungpook National University , Daegu, Korea (Republic of)

    Minho Lee

  6. Chinese Academy of Sciences , Beijing, China

    Derong Liu

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Phon-Amnuaisuk, S., Lee, SY. (2016). Investigating a Dictionary-Based Non-negative Matrix Factorization in Superimposed Digits Classification Tasks. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9949. Springer, Cham. https://doi.org/10.1007/978-3-319-46675-0_37

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-46675-0_37

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46674-3

  • Online ISBN: 978-3-319-46675-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation