A Self Supervised Bi-directional Neural Network (BDSONN) Architecture for Object Extraction Guided by Beta Activation Function and Adaptive Fuzzy Context Sensitive Thresholding

Authors: Siddhartha Bhattacharyya, Paramartha Dutta, Ujjwal Maulik, Prashanta Kumar Nandi

Abstract:

A multilayer self organizing neural neural network (MLSONN) architecture for binary object extraction, guided by a beta activation function and characterized by backpropagation of errors estimated from the linear indices of fuzziness of the network output states, is discussed. Since the MLSONN architecture is designed to operate in a single point fixed/uniform thresholding scenario, it does not take into cognizance the heterogeneity of image information in the extraction process. The performance of the MLSONN architecture with representative values of the threshold parameters of the beta activation function employed is also studied. A three layer bidirectional self organizing neural network (BDSONN) architecture comprising fully connected neurons, for the extraction of objects from a noisy background and capable of incorporating the underlying image context heterogeneity through variable and adaptive thresholding, is proposed in this article. The input layer of the network architecture represents the fuzzy membership information of the image scene to be extracted. The second layer (the intermediate layer) and the final layer (the output layer) of the network architecture deal with the self supervised object extraction task by bi-directional propagation of the network states. Each layer except the output layer is connected to the next layer following a neighborhood based topology. The output layer neurons are in turn, connected to the intermediate layer following similar topology, thus forming a counter-propagating architecture with the intermediate layer. The novelty of the proposed architecture is that the assignment/updating of the inter-layer connection weights are done using the relative fuzzy membership values at the constituent neurons in the different network layers. Another interesting feature of the network lies in the fact that the processing capabilities of the intermediate and the output layer neurons are guided by a beta activation function, which uses image context sensitive adaptive thresholding arising out of the fuzzy cardinality estimates of the different network neighborhood fuzzy subsets, rather than resorting to fixed and single point thresholding. An application of the proposed architecture for object extraction is demonstrated using a synthetic and a real life image. The extraction efficiency of the proposed network architecture is evaluated by a proposed system transfer index characteristic of the network.

Keywords: Beta activation function, fuzzy cardinality, multilayer self organizing neural network, object extraction,

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1080586

References:

[1] R. C. Gonzalez and P. Wintz, Digital Image Processing, MA: Addison- Wesley, 1977.
[2] A. Rosenfeld and A. C. Kak, Digital Picture Processing, vol. 1, 2nd eds., New York: Academic Press, 1982.
[3] M. P. Ekstrom, ed. Digital Image Processing Techniques, New York: Academic Press, 1984.
[4] M. R. Banham and A. K. Katsaggelos, Digital image restoration, IEEE Signal Processing Magazine, vol. 14, no. 2, pp. 24-41, 1997.
[5] M. Egmont-Petersen and T. Arts, "Recognition of radiopaque markers in X-ray images using a neural network as nonlinear fi lter," Pattern Recognition Letters, vol. 20, no. 5, pp. 521-533, 1999.
[6] S. Haykin, Neural networks: a comprehensive foundation, Macmillan College Publishing Co., New York, 1994.
[7] J. Hertz, A. Krogh, R. G. Palmer, Introduction to the theory of neural computation, Addison-Wesley, 1991.
[8] Y. H. Pao, Adaptive Pattern Recognition and Neural Networks, Addison- Wesley New York, 1989.
[9] G. L. Bilbro, M. White and W. Synder, "Image segmentation with neurocomputers," in Neural Computers eds. R. Eckmiller and C. V. D. Malsburg, Springer-Verlag New York, 1988.
[10] R. P. Lippmann, "An introduction to computing with neural nets," IEEE ASSP Magazine, pp. 3-22, 1987.
[11] T. Kim, V. Devarajan and M. Manry, "Road extraction from aerial images using neural networks," Proceedings of ASPRS Annual Convention, vol. 3, pp. 146-154.
[12] M. Antonucci, B. Tirozzi, N. D. Yarunin et al., "Numerical simulation of neural networks with translation and rotation invariant pattern recognition," International Journal of Modern Physics B, vol. 8, no. 11-12, pp. 1529-1541, 1994.
[13] G. A. Carpenter, and W. D. Ross, "ART-EMAP: A neural network architecture for object recognition by evidence accumulation," IEEE Transactions on Neural Networks, vol. 6, no. 4, pp. 805-818, 1995.
[14] T. Kohonen, "Self-organized formation of topologically correct feature maps," Biological Cybernetics, vol. 43, pp. 59-69, 1982. 15] T. Kohonen, "Self-organization and associative memory," Springer- Verlag, London, 1984.
[16] J. J. Hopfi eld, "Neurons with graded response have collective computational properties like those of two state neurons," Proceedings of Nat. Acad. Sci. U. S. pp. 3088-3092, 1984.
[17]
[6] B. Kosko, "Bidirectional associative memories," IEEE Transactions on Systems, Man and Cybernetics, vol. 18, no. 1, pp. 49-60, 1988.
[18] B. Kosko, Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence, Prentice-Hall Englewood Cliffs NJ, 1992.
[19] W. Chua and l. Yang, "Cellular network: theory," IEEE Transactions on Circuits and Systems, vol. 35, no. 10, pp. 1257-1282, 1988.
[20] W. Chua and L. Yang, "Cellular network: applications," IEEE Transactions on Circuits and Systems, vol. 35, no. 10, pp. 1273-1290, 1988.
[21] A. K. Datta, S. Munshi and S. Bhattacharyya, "Object Extraction In Artifi cial Retina Using Cellular Neural Network Optimized By Genetic Algorithm With Fuzziness Measure," Proceedings of International Conference on Fiber Optics and Photonics, vol. 2, pp. 723-725, 2000.
[22] G. A. Carpenter and S. Grossberg, Pattern Recognition by Self- Organizing Neural Networks, Cambridge, MA: MIT Press, 1991.
[23] A. Ghosh, N. R. Pal, and S. K. Pal, "Self-organization for object extraction using multilayer neural network and fuzziness measures," IEEE Transactions on Fuzzy Systems, vol. 1, no. 1, pp. 54-68, 1993.
[24] R. o. Duda and P. E. Hart, Pattern Classifi cation and Scene Analysis, New York: Wiley, 1973.
[25] J. T. Tou and R. C. Gonzalez, Pattern Recognition Principles, Reading, MA: Addison Wesley, 1974.
[26] S. Bhattacharyya, P. Dutta and U. Maulik, "Multiple Target Tracking Using Self-Organizing Neural Network Based Segmentation of Optical Flow Field," Proceedings of Conference of Recent Trends In Manufacturing, pp. 369-376, 2003.
[27] A. Ghosh and A. Sen, "Self organizing neural network for multi-level image segmentation," Soft Computing Applications to Pattern Recognition and Image Processing, eds. A. Ghosh and S. K. Pal, pp. 129-144, World Scientifi c, 2002.
[28] S. Bhattacharyya, U. Maulik and S. Bandyopadhyay, "A Fuzzy Cardinality Based Approximation for Extracting Multi-Scale Objects From Noisy Background Using Self Organizing Neural Network," Proceedings of International Conference on Communications, Devices and Intelligent Systems, pp. 461-464, 2004.
[29] S. Bhattacharyya, P. Dutta and U. Maulik, "Multi-Scale Object Extraction Using Self Organizing Neural Network With A Multi-Level Sigmoidal Activation Function," Proceedings of 5th International Conference on Advances in Pattern Recognition, pp. 435-438, 2003.
[30] P. Dutta, S. Bhattacharyya and K. Dasgupta, "Multi-Scale Object Extraction Using A Self Organizing Neural Network With A Multi-Level Beta Activation Function," Proceedings of International Conference on Intelligent Sensing and Information Processing, pp. 139-142, 2004.
[31] S. Bhattacharyya, P. Dutta and D. DuttaMajumder, "Multiscale Object Extraction Using A Self-Organizing Neural Network With Multilevel Beta Activation Function and its Sigmoidal Counterpart: A Comparative Study," Proceedings of International Conference on Recent Trends and New Directions of Research in Cybernetics & Systems Theory, 2004.
[32] S. Bhattacharyya and P. Dutta, "Multiscale Object Extraction with MUSIG and MUBET with CONSENT: A Comparative Study," Proceedings of KBCS 2004, pp. 100-109, India.
[33] S. Bhattacharyya and P. Dutta, "XMUBET with CONSENT: A Pixel Hostility Induced Multiscale Object Extractor," Proceedings of IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 04), pp. 277-282, 2004.
[34] S. Bhattacharyya and P. Dutta, "XMUSIG with CONSENT: Pixel Hostility Induced Multiscale Object Extractor," Proceedings of International Conference on Intelligent Sensing and Information Processing (ICISIP 2005), 2005.
[35] S. Bhattacharyya and K. Dasgupta, "Color Object Extraction From A Noisy Background Using Parallel Multi-layer Self-Organizing Neural Networks," Proceedings of CSI-YITPA(E) 2003, pp. 32-36, 2003.
[36] S. Bhattacharyya and P. Dutta, "A Parallel Self-Organizing Neural Net work Architecture For Extraction of Graded Color Objects," Proceedings of International Conference on Recent Trends and New Directions of Research in Cybernetics & Systems Theory, 2004.
[37] S. Bhattacharyya, P. Dutta and U. Maulik, "Graded color object extraction by a Parallel Self-Organizing Neural Network (PSONN) architecture guided by a MUBET activation function," Proceedings of ICIS 2005, 2005.
[38] S. Bhattacharyya, P. Dutta and U. Maulik, "MUSIG and MUBET guided PSONN based color object extraction: A Comparative Study," Proceedings of International Conference on Information Technology 2005, 2005.
[39] S. Bhattacharyya, P. Dutta and P. Nandi, "True Color Object Extraction By A Parallel Self Organizing Neural Network (PSONN) Architecture Guided By XMUBET With CONSENT," Proceedings of EAIT 2006, 2006.
[40] S. Bhattacharyya and P. Dutta, "Designing pruned multilayer self organizing neural network (MLSONN) for object extraction from a noisy background," Book of Abstracts of SOOP 05, pp. 131-132, 2005.
[41] S. Bhattacharyya and P.Dutta, "Designing pruned neighborhood neural networks for object extraction from noisy background," International Journal of Foundations of Computing and Decision Sciences, vol. 31, no. 2, pp. 105-134, 2006.
[42] L. A. Zadeh, "Fuzzy sets," Inform. and Control, vol. 8, no. 1, pp.338- 353, 1965.
[43] T. J. Ross and T. Ross, Fuzzy Logic With Engineering Applications, McGraw Hill College Div., 1995.
[44] A. Deluca and S. Termini, "A defi nition of non probabilistic entropy in the setting of fuzzy set theory," Information and Control, vol. 20, pp. 301-312, 1972.