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Object Recognition Using Reflex Fuzzy Min-Max Neural Network with Floating Neurons

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Computer Vision, Graphics and Image Processing

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4338))

Abstract

This paper proposes an object recognition system that is invariant to rotation, translation and scale and can be trained under partial supervision. The system is divided into two sections namely, feature extraction and recognition sections. Feature extraction section uses proposed rotation, translation and scale invariant features. Recognition section consists of a novel Reflex Fuzzy Min-Max Neural Network (RFMN) architecture with “Floating Neurons”. RFMN is capable to learn mixture of labeled and unlabeled data which enables training under partial supervision. Learning under partial supervision is of high importance for the practical implementation of pattern recognition systems, as it may not be always feasible to get a fully labeled dataset for training or cost to label all samples is not affordable. The proposed system is tested on shape data-base available online, Marathi and Bengali digits. Results are compared with “General Fuzzy Min-Max Neural Network” proposed by Gabrys and Bargiela.

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References

  1. Zadeh, L.A.: Fuzzy Sets. Information and control 8, 338–353 (1965)

    Article  MATH  MathSciNet  Google Scholar 

  2. Simpson, P.K.: Fuzzy Min-Max Neural Network – Part I: Classification. IEEE Tran. on Neural Networks 3(5), 776–786 (1992)

    Article  Google Scholar 

  3. Simpson, P.K.: Fuzzy Min-Max Neural Network – Part II: Clustering. IEEE Tran. Fuzzy System 1(1), 32–45 (1993)

    Article  Google Scholar 

  4. Gabrys, B., Bargiela, A.: General fuzzy min-max neural network for clustering and classification. IEEE Tran. Neural Network 11, 769–783 (2000)

    Article  Google Scholar 

  5. Kauppinen, H.,, Seppanen, T., Pietikamen, M.: An experimental comparison of Autoregressive and Fourier Based Descriptors in 2D shape classification. IEEE Trans. Pattern Analysis, Machine Intelligence 17, 207–210 (1995)

    Article  Google Scholar 

  6. Perantonis, S.J.: Translation, rotation and scale invariant pattern recognition by high-order neural networks and moment classifiers. IEEE Trans. Neural Networks 4, 276–283 (1993)

    Article  Google Scholar 

  7. The, C.H., Chin, R.T.: On image analysis by the methods of moments. IEEE Trans. Pattern Analysis and Machine Intelligence 10, 496–513 (1988)

    Article  Google Scholar 

  8. Torres-Mendez, L.A., Ruiz-Suarez, J.C., Sucar, L.E., Gomez, G.: Translation, rotation and scale-invariant object recognition. IEEE Trans. on Systems, Man and Cybernetics 30(1), 5–130 (2000)

    Google Scholar 

  9. Nandedkar, A.V., Biswas, P.K.: A Fuzzy min-max neural network classifier with compensatory neuron architecture. In: 17th Int. Cnf. on Pattern Recognition (ICPR 2004), Cambridge UK, August 2004, pp. 553–556 (2004)

    Google Scholar 

  10. Nandedkar, A.V., Biswas, P.K.: A General fuzzy min max neural network with compensatory neuron architecture. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds.) KES 2005. LNCS (LNAI), vol. 3683, pp. 1160–1167. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  11. Baitsell, G.A.: Human Biology, 2nd edn. Mc-Graw Hill Book co. inc., New York (1950)

    Google Scholar 

  12. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classification, 2nd edn. John Wiley &Sons Inc., Singapore (2001)

    MATH  Google Scholar 

  13. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Pearson Education Pvt. Ltd., Delhi (2002)

    Google Scholar 

  14. Sebastian, T., Klein, P., Kimia, B.: Recognition of shapes by editing shock graphs. In: 18th Int. Conf. on Computer Vision (ICCV 2001), vol. 1, pp. 755–762 (2001), http://www.lems.brown.edu/vision/researchAreas/SIID/

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© 2006 Springer-Verlag Berlin Heidelberg

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Nandedkar, A.V., Biswas, P.K. (2006). Object Recognition Using Reflex Fuzzy Min-Max Neural Network with Floating Neurons. In: Kalra, P.K., Peleg, S. (eds) Computer Vision, Graphics and Image Processing. Lecture Notes in Computer Science, vol 4338. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11949619_53

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  • DOI: https://doi.org/10.1007/11949619_53

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-68301-8

  • Online ISBN: 978-3-540-68302-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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