Skip to main content

Incremental Self-Organizing Map (iSOM) in Categorization of Visual Objects

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7664)

Abstract

We present a modification of the well-known Self-Organizing Map (SOM) in which we incrementally allocate the neuronal nodes to progressively added new stimuli. Our incremental SOM (iSOM) aims at the situation when a stimulus, or percept, is represented by a number of neuronal nodes a typical case in biological situation when the redundancy of representation of data is important. The iSOM is applied to categorization of visual objects using the recently introduced feature vector based on the angular integral of the Radon transform [10].

Keywords

  • Self-organizing maps
  • Incremental learning
  • Radon transform

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (Canada)
  • 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Alahakoon, D., Halgamuge, S.K., Srinivasan, B.: Dynamic Self-organizing Maps with Controlled Growth for Knowledge Discovery. IEEE Trans. Neural Networks 11(3), 601–614 (2000)

    CrossRef  Google Scholar 

  2. Blackmore, J., Miikkulainen, R.: Incremental Grid Growing: Encoding High-Dimensional Structure into a Two-Dimensional Feature Map. In: Proc. IEEE Int. Conf. Neural Networks, pp. 450–455 (1993)

    Google Scholar 

  3. Bosch, A., Zisserman, A., Munoz, X.: Image Classification Using Random Forests and Ferns. In: 11th Int. Conf. Computer Vision, vol. 21(4), pp. 1–8 (2007)

    Google Scholar 

  4. Fritzke, B.: Growing Cell Structures — a Self-Organizing Network for Unsupervised and Supervised Learning. Neural Networks 7(9), 1441–1460 (1994)

    CrossRef  Google Scholar 

  5. Fritzke, B.: A Growing Natural Gas Network Learns Topologies. In: Advances in Neural Information Processing, vol. 7, pp. 625–632. MIT Press (1995)

    Google Scholar 

  6. Jantvik, T., Gustafsson, L., Papliński, A.P.: A Self-Organized Artificial Neural Network Architecture for Sensory Integration with Applications to Letter–Phoneme Integration. Neural Computation 23, 2101–2139 (2011)

    CrossRef  Google Scholar 

  7. Kohonen, T.: Self-Organising Maps, 3rd edn. Springer, Berlin (2001)

    CrossRef  Google Scholar 

  8. Lee, J.A., Verleysen, M.: Nonlinear Dimensionality Reduction. Springer (2007)

    Google Scholar 

  9. Lowe, D.G.: Distinctive Image Features From Scale-Invariant Keypoints. Int. J. Comp. Vision 60(2), 91–110 (2004)

    CrossRef  Google Scholar 

  10. Papliński, A.P.: Rotation Invariant Categorization of Colour Images Using Radon Transform. In: Proc. WCCI–IJCNN, pp. 1408–1413. IEEE (2012)

    Google Scholar 

  11. Papliński, A.P., Gustafsson, L., Mount, W.M.: A Model of Binding Concepts to Spoken Names. Aust. Journal of Intelligent Information Processing Systems 11(2), 1–5 (2010)

    Google Scholar 

  12. Papliński, A.P., Gustafsson, L., Mount, W.M.: A Recurrent Multimodal Network for Binding Written Words and Sensory-Based Semantics into Concepts. In: Lu, B.-L., Zhang, L., Kwok, J. (eds.) ICONIP 2011, Part I. LNCS, vol. 7062, pp. 413–422. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  13. Radon, J.: Radon transform, http://en.wikipedia.org/wiki/Radon_transform

  14. Sammon, J.W.: A Nonlinear Mapping for Data Structure Analysis. IEEE Trans. Computers 18(5), 401–409 (1969)

    CrossRef  Google Scholar 

  15. Serre, T., Wolf, L., Poggio, T.: Object Recognition with Features Inspired by Visual Cortex. In: IEEE Comp. Soc. Conf. Comp. Vision and Patt. Recognition, vol. 2, pp. 994–1000 (2005)

    Google Scholar 

  16. Shah-Hosseini, H., Safabakhsh, R.: TASOM: A New Time Adaptive Self-Organizing Map. IEEE Trans. Syst., Man, Cyber. B 33(2), 271–282 (2003)

    CrossRef  Google Scholar 

  17. Shah-Hosseini, H., Safabakhsh, R.: Binary Tree Time Adaptive Self-Organizing Map. Neurocomputing 74, 1823–1839 (2011)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Papliński, A.P. (2012). Incremental Self-Organizing Map (iSOM) in Categorization of Visual Objects. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7664. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34481-7_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-34481-7_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34480-0

  • Online ISBN: 978-3-642-34481-7

  • eBook Packages: Computer ScienceComputer Science (R0)