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2005 | 15 | 1 | 99-114
Tytuł artykułu

Image recall using a large scale generalized Brain-State-in-a-Box neural network

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
An image recall system using a large scale associative memory employing the generalized Brain-State-in-a-Box (gBSB) neural network model is proposed. The gBSB neural network can store binary vectors as stable equilibrium points. This property is used to store images in the gBSB memory. When a noisy image is presented as an input to the gBSB network, the gBSB net processes it to filter out the noise. The overlapping decomposition method is utilized to efficiently process images using their binary representation. Furthermore, the uniform quantization is employed to reduce the size of the data representation of the images. Simulation results for monochrome gray scale and color images are presented. Also, a hybrid gBSB-McCulloch-Pitts neural model is introduced and an image recall system is built around this neural net. Simulation results for this model are presented and compared with the results for the system employing the gBSB neural model.
Rocznik
Tom
15
Numer
1
Strony
99-114
Opis fizyczny
Daty
wydano
2005
otrzymano
2004-08-16
poprawiono
2005-01-11
Twórcy
autor
  • School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana 47907-2035, USA
  • School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana 47907-2035, USA
Bibliografia
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  • Anderson J.A., Silverstein J.W., Ritz S.A. and Jones R.S. (1989): Distinctive features, categorical perception, probability learning: Some applications of a neural model, In: Neurocomputing; Foundations of Research (J.A. Anderson and E. Rosenfeld, Eds.). - Cambridge, MA: The MIT Press, ch. 22, pp. 283-325, reprint from Psych. Rev. 1977, Vol. 84, pp. 413-451.
  • Golden R.M. (1993): Stability and optimization analyses of the generalized Brain-State-in-a-Box neural network model. - J. Math. Psych., Vol. 37, No. 2, pp. 282-298.
  • Gonzalez R.C. and Wintz P. (1987): Digital Image Processing, 2nd Ed. - Reading: Addison-Wesley.
  • Gray R.M. and Neuhoff D.L. (1998): Quantization. - IEEE Trans. Inf. Theory, Vol. 44, No. 6, pp. 2325-2383.
  • Hassoun M.H. (1995): Fundamentals of Artificial Neural Networks. - Cambridge: A Bradford Book, The MIT Press.
  • Hui S. and .Żak S.H. (1992): Dynamical analysis of the Brain-State-in-a-Box (BSB) neural models. - IEEE Trans. Neural Netw., Vol. 3, No. 1, pp. 86-94.
  • Ikeda M. and Šiljak D.D. (1980): Overlapping decompositions, expansions and contractions of dynamic systems. - Large Scale Syst., Vol. 1, No. 1, pp. 29-38.
  • Ikeda N., Watta P., Artiklar M. and Hassoun M.H. (2001): A two-level Hamming network for high performance associative memory. - Neural Netw., Vol. 14, No. 9, pp. 1189-1200.
  • Lillo W.E., Miller D.C., Hui S. and .Żak S.H. (1994): Synthesis of Brain-State-in-a-Box (BSB) based associative memories. - IEEE Trans. Neural Netw., Vol. 5, No. 5, pp. 730-737.
  • Oh C. and Żak S.H. (2002): Large scale neural associative memorydesign. -Przegląd Elektrotechniczny (Electrotechnical Review), Vol. 2002, No. 10, pp. 220-225.
  • Oh C. and Żak S.H. (2003): Associative memory design using overlapping decomposition and generalized Brain-State-in-a-Box neural networks. - Int. J. Neural Syst., Vol. 13, No. 3, pp. 139-153.
  • Park J. and Park Y. (2000): An optimization approach to design of generalized BSB neural associative memories. - Neural Comput., Vol. 12, No. 6, pp. 1449-1462.
  • Park J., Cho H. and Park D. (1999): Design of GBSB neural associative memories using semidefinite programming. - IEEE Trans. Neural Netw., Vol. 10, No. 4, pp. 946-950.
  • Sayood K. (1996): Introduction to Data Compression. - San Francisco: Morgan Kaufmann.
  • Schultz A. (1993): Collective recall via the Brain-State-in-a-Boxnetwork. -IEEE Trans. Neural Netw., Vol. 4, No. 4, pp. 580-587.
  • Zetzsche C. (1990): Sparse coding: the link between low level vision and associative memory, In: Parallel Processing in Neural Systems and Computers, (R. Eckmiller, G. Hartmann and G. Hauske, Eds.). - Amsterdam: Elsevier, pp. 273-276.
Typ dokumentu
Bibliografia
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