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

International Journal of Applied Mathematics and Computer Science (2005)

- Volume: 15, Issue: 1, page 99-114
- ISSN: 1641-876X

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topOh, Cheolhwan, and Żak, Stanisław. "Image recall using a large scale generalized Brain-State-in-a-Box neural network." International Journal of Applied Mathematics and Computer Science 15.1 (2005): 99-114. <http://eudml.org/doc/207732>.

@article{Oh2005,

abstract = {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.},

author = {Oh, Cheolhwan, Żak, Stanisław},

journal = {International Journal of Applied Mathematics and Computer Science},

keywords = {image recall; overlapping decomposition; associative memory; Brain-State-in-a-Box(BSB) neural network},

language = {eng},

number = {1},

pages = {99-114},

title = {Image recall using a large scale generalized Brain-State-in-a-Box neural network},

url = {http://eudml.org/doc/207732},

volume = {15},

year = {2005},

}

TY - JOUR

AU - Oh, Cheolhwan

AU - Żak, Stanisław

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

JO - International Journal of Applied Mathematics and Computer Science

PY - 2005

VL - 15

IS - 1

SP - 99

EP - 114

AB - 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.

LA - eng

KW - image recall; overlapping decomposition; associative memory; Brain-State-in-a-Box(BSB) neural network

UR - http://eudml.org/doc/207732

ER -

## References

top- Akar M. and Sezer M.E. (2001): Associative memory design using overlapping decompositions. - Automatica, Vol. 37, No. 4, pp. 581-587. Zbl0976.68128
- Anderson J.A. (1995): An Introduction to Neural Networks. -Cambridge: A Bradford Book, The MIT Press. Zbl0850.68263
- 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. Zbl0771.92004
- 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. Zbl0850.68271
- 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. Zbl0443.93009
- 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. Zbl0858.94009
- 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.

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