Advertisement

Adaptive-Neuro Fuzzy Inference System for Human Posture Classification Using a Simplified Shock Graph

  • S. Shahbudin
  • A. Hussain
  • Ahmed El-Shafie
  • N. M. Tahir
  • S. A. Samad
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5857)

Abstract

In this paper, a neuro-fuzzy technique known as the Adaptive-Neuro Fuzzy Inference System (ANFIS) has been used to highlight the application of ANFIS to perform human posture classification task using the new simplified shock graph (SSG) representation. Basically, a shock graph is a shape abstraction that decomposed a shape into a set of hierarchically organized primitive parts. The shock graph that represents the silhouette of an object in terms of a set of qualitatively defined parts and organized in a hierarchical, directed acyclic graph is used as a powerful representation of human shape in our work. The SSG feature provides a compact, unique and simple way of representing human shape and has been tested with several classifiers. As such, in this paper we intend to test its efficacy with another classifier, that is, the ANFIS classifier system. The result showed that the proposed ANFIS model can be used in classifying various human postures.

Keywords

Adaptive-Neuro Fuzzy Inference System (ANFIS) simplified shock graph (SSG) Artificial Neural Network (ANN) 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Jang, J.-S.R.: ANFIS: Adaptive-Network-based Fuzzy Inference Systems. IEEE Transactions on Systems, Man, and Cybernetics 23(3), 665–685 (1993)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Jang, J.-S.R., Sun, C.-T., Mizutani, E.: Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice Hall, US (1997)Google Scholar
  3. 3.
    Tahir, N.M., Hussain, A., Samad, S.A., Husain, H.: Shock Graph Representation and Modelling of Posture. ETRI Journal 29(4) (2007)Google Scholar
  4. 4.
    Shahbudin, S., Hussain, A., Tahir, N.M., Samad, S.A.: Multi-class Support Vector Machine for Human Posture Classification Using a Simplified Shock Graph. In: 2008 International Symposium on Information Theory and its Applications (2008)Google Scholar
  5. 5.
    Tahir, N.M., Hussain, A.: Human Shape Analysis Using Artificial Neural Network. In: Proc. of ICOM 2005, Kuala Lumpur (2005)Google Scholar
  6. 6.
    Lin, C.-J., Wang, J.-G., Lee, C.-Y.: Pattern recognition using neural- fuzzy networks based on improved particle swam optimization. Expert systems with application 36(3), 5402–5410 (2009)CrossRefGoogle Scholar
  7. 7.
    Bailador, G., Guadarrama, S.: Robust Gesture Recognition using a Prediction-Error-Classification Approach. In: IEEE International Fuzzy Systems Conference, FUZZ-IEEE 2007, pp. 1–7 (2007)Google Scholar
  8. 8.
  9. 9.
    Virant-Klun, I., Virant, J.: Fuzzy logic alternative for analysis in the biomedical sciences. Comput. Biomed. Res. 32, 305–321 (1999)CrossRefGoogle Scholar
  10. 10.
    Siddiqi, K., Kimia, B.B.: A Shock Grammar for Recognition. In: Proc. of the IEEE Conf. Computer Vision and Pattern Recognition, San Francisco, June 1996, pp. 507–513 (1996)Google Scholar
  11. 11.
    Belongie, S., Malik, J., Puzicha, J.: Matching Shapes. In: Proc. of IEEE Int’l. Conf. Computer Vision, pp. 454–461 (2001)Google Scholar
  12. 12.
    Sidiqqi, K., Shokoufandeh, A., Dickinson, S.J., Zucker, S.W.: Shock Graphs and Shape Matching. Int’l J. of Computer Vision 35(1), 13–32 (1999)CrossRefGoogle Scholar
  13. 13.
    Sebastian, T.B., Klein, P.N., Kimia, B.B.: Recognition of Shapes by Editing Their Shock Graphs. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(5), 550–571 (2004)CrossRefGoogle Scholar
  14. 14.
    Klein, P.N., Sebastian, T.B., Kimia, B.B.: Shape matching using edit- distance: an implementation. In: Symposium on Discrete Algorithms in Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms, pp. 781–790 (2001)Google Scholar
  15. 15.
    Klein, P.N., Tirthapura, S., Sharvit, D., Kimia, B.B.: A tree-edit distance algorithm for comparing simple, closed shapes. In: Symposium on Discrete Algorithms in Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms, pp. 696–704 (2000)Google Scholar
  16. 16.
    Xu, W., Li, L., Zou, S.: Detection and Classification of Microcalcifications Based on DWT and ANFIS. In: The 1st International Conference on Bioinformatics and Biomedical Engineering, 2007. ICBBE 2007, July 6-8, pp. 547–550 (2007)Google Scholar
  17. 17.
    Übeyli, E.D.: Adaptive Neuro-Fuzzy Inference Systems for Automatic Detection of Breast Cancer. Journal of Medical System 18, 157–174 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • S. Shahbudin
    • 1
  • A. Hussain
    • 1
  • Ahmed El-Shafie
    • 1
  • N. M. Tahir
    • 2
  • S. A. Samad
    • 1
  1. 1.Department of Electrical, Electronics & Systems Engineering, Faculty of EngineeringUniversiti Kebangsaan MalaysiaBangiMalaysia
  2. 2.Department of Computer, Faculty of Electrical EngineeringTechnology University of MaraShah AlamMalaysia

Personalised recommendations