Skip to main content

Serial Multiple Classifier Systems Exploiting a Coarse to Fine Output Coding

  • Conference paper
  • First Online:
Multiple Classifier Systems (MCS 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2709))

Included in the following conference series:

Abstract

We investigate serial multiple classifier system architectures which exploit a hierarchical output coding. Such architectures are known to deliver performance benefits and are widely used in applications involving a large number of classes such as character and handwriting recognition. We develop a theoretical model which underpins this approach to multiple classifier system design and show how it relates to various heuristic design strategies advocated in the literature. The approach is applied to the problem of 3D object recognition in computer vision.

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

Access this chapter

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 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. A. Ahmadyfard and J. Kittler. Enhancement of ARG object recognition method. In Proceeding of 11 the European Signal Processing Conference, volume 3, pages 551–554, September 2002.

    Google Scholar 

  2. A. R. Ahmadyfard and J. Kittler. Amultiple classifier system approach to affine invariant object recognition. In 3rd International Conference on Computer Vision Systems, 2003 (accepted).

    Google Scholar 

  3. M C Fairhurst and A F R Rahman. Generalised approach to the recognition of structurally similar handwritten characters using multiple expert classifiers. IEE Proc. on Vision, Image and Signal Processing, 144(1):15–22, 2 1997.

    Article  Google Scholar 

  4. http://www.ee.surrey.ac.uk/Research/VSSP/demos/colour/soil47/.

    Google Scholar 

  5. Ianakiev K. and V. Govindaraju. Architecture for classifier combination using entropy measures. In IAPR International Workshop on Multiple Classifier Systems, Lecture Notes in Computer Science, pages 340–350, June 2000.

    Chapter  Google Scholar 

  6. G Kim and V Govindaraju. A lexicon driven approach to handwritten word recognition. IEEE Transactions on PAMI, 19(4):366–379, 1997.

    Google Scholar 

  7. I Krassimir and V Govindaraju. An architecture for classifier combination using entropy measure. In J Kittler and F Roli, editors, Proceedings of Multple Classifier Systems 2000, pages 340–350, 2000.

    Google Scholar 

  8. S Madhvanath, E Kleinberg, and V Govindaraju. Holistic verification for handwritten phrases. IEEE Transactions on PAMI, 21(12):1344–1356, 1999.

    Google Scholar 

  9. J. Matas, D. Koubaroulis, and J. Kittler. Colour image retrieval and object recognition using the multimodal neighbourhood signature. In D. Vernon, editor, Proceedings of ECCV, volume Springer, pages 48–64, 2000.

    Google Scholar 

  10. A F R Rahman and M C Fairhurst. Exploiting second order information to design a novel multiple expert decision combination platform for pattern classification. Electronic Letters, 33:476–477, 1997.

    Article  Google Scholar 

  11. A F R Rahman and M C Fairhurst. A new hybrid approach in combining multiple experts to recognise handwritten numerals. Pattern Recognition Letters, 18:781–790, 1997.

    Article  Google Scholar 

  12. A F R Rahman and M C Fairhurst. An evaluation of multi-expert configurations for for the recognition of handwritten numerals. Pattern Recognition, 31:1255–1273, 1998.

    Article  Google Scholar 

  13. A F R Rahman and M C Fairhurst. Enhancing multiple expert decision combination strategies through exploitation of a priori information sources. In IEE PROCEEDINGS-VISION IMAGE AND SIGNAL PROCESSING, volume 146, pages 40–49, 1999.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kittler, J., Ahmadyfard, A., Windridge, D. (2003). Serial Multiple Classifier Systems Exploiting a Coarse to Fine Output Coding. In: Windeatt, T., Roli, F. (eds) Multiple Classifier Systems. MCS 2003. Lecture Notes in Computer Science, vol 2709. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44938-8_11

Download citation

  • DOI: https://doi.org/10.1007/3-540-44938-8_11

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40369-2

  • Online ISBN: 978-3-540-44938-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics