Skip to main content
Springer Nature Link
Log in

Orthogonal Nonnegative Matrix Factorization for Blind Image Separation

  • Conference paper
Advances in Visual Informatics (IVIC 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8237))

Included in the following conference series:

Abstract

This paper describes an application of orthogonal nonnegative matrix factorization (NMF) algorithm in blind image separation (BIS) problem. The algorithm itself has been presented in our previous work as an attempt to provide a simple and convergent algorithm for orthogonal NMF, a type of NMF proposed to improve clustering capability of the standard NMF. When we changed the application domain of the algorithm to the BIS problem, surprisingly good results were obtained; the reconstructed images were more similar to the original ones and pleasant to view compared to the results produced by other NMF algorithms. Good results were also obtained when another dataset that consists of unrelated images was used. This practical use along with its convergence guarantee and implementation simplicity demonstrate the benefits of our algorithm.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Ding, C., Li, T., Peng, W., Park, H.: Orthogonal nonnegative matrix tri-factorizations for clustering. In: 12th ACM SIGKDD Intl Conf. on Knowledge Discovery and Data Mining, pp. 126–135 (2006)

    Google Scholar 

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

    Article  Google Scholar 

  3. Lee, D., Seung, H.: Algorithms for non-negative matrix factorization. In: Proc. Advances in Neural Processing Information Systems, pp. 556–562 (2000)

    Google Scholar 

  4. Mirzal, A.: A convergent algorithm for orthogonal nonnegative matrix factorization. Submitted to J. Computational and Applied Mathematics

    Google Scholar 

  5. Mirzal, A.: Nonnegative matrix factorizations for clustering and LSI: Theory and programming. LAP LAMBERT Academic Publishing, Germany (2011)

    Google Scholar 

  6. Lin, C.: On the convergence of multiplicative update algorithms for nonnegative matrix factorization. IEEE Transactions on Neural Networks 18(6), 1589–1596 (2007)

    Article  Google Scholar 

  7. Pauca, V., Piper, J., Plemmons, R.: Nonnegative matrix factorization for spectral data analysis. Linear Algebra and Its Applications 416(1), 29–47 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  8. Cichocki, A., Amari, S.-i., Zdunek, R., Kompass, R., Hori, G., He, Z.: Extended smart algorithms for non-negative matrix factorization. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 548–562. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  9. Kim, J., Park, H.: Toward faster nonnegative matrix factorization: A new algorithm and comparisons. In: 8th IEEE Intl Conf. on Data Mining, pp. 353–362 (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Computing N28-439-03, Universiti Teknologi Malaysia, 81310 UTM, Johor Bahru, Malaysia

    Andri Mirzal

Authors
  1. Andri Mirzal
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Visual Informatics, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia

    Halimah Badioze Zaman

  2. Computer Laboratory, University of Cambridge, CB3 0FD, Cambridge, UK

    Peter Robinson

  3. School of Computing Science, Newcastle University, Culture Lab, King’s Walk, NE1 7RU, Newcastle Upon Tyne, UK

    Patrick Olivier

  4. Dept. of Computer Science and Information Engineering, National Central University, No 300, Jhongda Rd, Jhongli, Taiwan

    Timothy K. Shih

  5. Computing and Information Systems, Kingston University, Penrhyn Road, Kingston upon Thames, KT1 2EE, London, United Kingdom

    Sergio Velastin

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer International Publishing Switzerland

About this paper

Cite this paper

Mirzal, A. (2013). Orthogonal Nonnegative Matrix Factorization for Blind Image Separation. In: Zaman, H.B., Robinson, P., Olivier, P., Shih, T.K., Velastin, S. (eds) Advances in Visual Informatics. IVIC 2013. Lecture Notes in Computer Science, vol 8237. Springer, Cham. https://doi.org/10.1007/978-3-319-02958-0_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-02958-0_3

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02957-3

  • Online ISBN: 978-3-319-02958-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics