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Region Based Image Preprocessor for Feed-Forward Perceptron Based Systems

  • Keith A. Greenhow
  • Colin G. Johnson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8866)

Abstract

In this paper, we investigate the notion that there may be alternate methods, beyond typical rectilinear interpolations such as Bilinear Interpolation, that have a greater suitability for use in visual/image preprocessors for Artificial Neural Networks. We present a novel method for down-sampling image data in preparation for a Feed-Forward Perceptron system assisted by a neural usefulness metric, inspired by those common to pruning algorithms. This new method achieves greater accuracy compared to the same system using by Bilinear Interpolation, and has a reduced computational time.

Keywords

Artificial neural networks Preprocessor Image processing Salience Relevance assessment 

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References

  1. 1.
    Shibata, K., Utsunomiya, H.: Discovery of pattern meaning from delayed rewards by reinforcement learning with a recurrent neural network. In: The 2011 International Joint Conference on Neural Networks (IJCNN), pp. 1445–1452 (2011)Google Scholar
  2. 2.
    Davies, S., Patterson, C., Galluppi, F., Rast, A., Lester, D., Furber, S.: Interfacing real-time spiking i/o with the spinnaker neuromimetic architecture. Australian Journal of Intelligent Information Processing System 11, 7–11 (2010)Google Scholar
  3. 3.
    Mozer, M.C., Smolensky, P.: Using relevance to reduce network size automatically. Connection Science 1(1), 3–16 (1989)CrossRefGoogle Scholar
  4. 4.
    Mozer, M.C., Smolensky, P.: Skeletonization: a technique for trimming the fat from a network via relevance assessment. Advances in Neural Information Processing Systems 1, 107–115 (1989)Google Scholar
  5. 5.
    Engelbrecht, A., Cloete, I., Zurada, J.: Determining the significance of input parameters using sensitivity analysis. In: Mira, J., Sandoval, F. (eds.) From Natural to Artificial Neural Computation. LNCS, vol. 930, pp. 382–388. Springer, Berlin Heidelberg (1995)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.School of ComputingUniversity of KentCanterbury, KentUK

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