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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Zadeh, L.A.: Fuzzy Sets. Information and control 8, 338–353 (1965)
Simpson, P.K.: Fuzzy Min-Max Neural Network – Part I: Classification. IEEE Tran. on Neural Networks 3(5), 776–786 (1992)
Simpson, P.K.: Fuzzy Min-Max Neural Network – Part II: Clustering. IEEE Tran. Fuzzy System 1(1), 32–45 (1993)
Gabrys, B., Bargiela, A.: General fuzzy min-max neural network for clustering and classification. IEEE Tran. Neural Network 11, 769–783 (2000)
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)
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)
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)
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)
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)
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)
Baitsell, G.A.: Human Biology, 2nd edn. Mc-Graw Hill Book co. inc., New York (1950)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classification, 2nd edn. John Wiley &Sons Inc., Singapore (2001)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Pearson Education Pvt. Ltd., Delhi (2002)
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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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
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