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.
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References
Jang, J.-S.R.: ANFIS: Adaptive-Network-based Fuzzy Inference Systems. IEEE Transactions on Systems, Man, and Cybernetics 23(3), 665–685 (1993)
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)
Tahir, N.M., Hussain, A., Samad, S.A., Husain, H.: Shock Graph Representation and Modelling of Posture. ETRI Journal 29(4) (2007)
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)
Tahir, N.M., Hussain, A.: Human Shape Analysis Using Artificial Neural Network. In: Proc. of ICOM 2005, Kuala Lumpur (2005)
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)
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)
Data Analysis using Fuzzy Inference System, http://www3.ntu.edu.sg/home/aswduch/Teaching/Assign2/ZhaoGuopeng.pdf
Virant-Klun, I., Virant, J.: Fuzzy logic alternative for analysis in the biomedical sciences. Comput. Biomed. Res. 32, 305–321 (1999)
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)
Belongie, S., Malik, J., Puzicha, J.: Matching Shapes. In: Proc. of IEEE Int’l. Conf. Computer Vision, pp. 454–461 (2001)
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)
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)
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)
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)
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)
Übeyli, E.D.: Adaptive Neuro-Fuzzy Inference Systems for Automatic Detection of Breast Cancer. Journal of Medical System 18, 157–174 (2008)
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Shahbudin, S., Hussain, A., El-Shafie, A., Tahir, N.M., Samad, S.A. (2009). Adaptive-Neuro Fuzzy Inference System for Human Posture Classification Using a Simplified Shock Graph. In: Badioze Zaman, H., Robinson, P., Petrou, M., Olivier, P., Schröder, H., Shih, T.K. (eds) Visual Informatics: Bridging Research and Practice. IVIC 2009. Lecture Notes in Computer Science, vol 5857. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05036-7_55
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DOI: https://doi.org/10.1007/978-3-642-05036-7_55
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