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Proceedings Paper

Binary image compression using identity mapping backpropagation neural network
Author(s): Nabeel A. Murshed; Flavio Bortolozzi; Robert Sabourin
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Paper Abstract

This work proposes a method for using an identity-mapping backpropagation (IMBKP) neural network for binary image compression, aimed at reducing the dimension of the feature vector in a NN-based pattern recognition system. In the proposed method, the IMBKP network was trained with the objective of achieving good reconstruction quality and a reasonable amount of image compression. This criteria is very important, when using binary images as feature vectors. Evaluation of the proposed network was performed using 800 images of handwritten signatures. The lowest and highest reconstruction errors were, respectively, 3.05 multiplied by 10-3% and 0.01%. The proposed network can be used to reduce the dimension of the input vector to a NN-based pattern recognition system without almost and degradation and, yet, with a good reduction in the number of input neurons.

Paper Details

Date Published: 1 April 1997
PDF: 7 pages
Proc. SPIE 3030, Applications of Artificial Neural Networks in Image Processing II, (1 April 1997); doi: 10.1117/12.269779
Show Author Affiliations
Nabeel A. Murshed, Pontificia Univ. Catolica do Parana (Brazil)
Flavio Bortolozzi, Pontificia Univ. Catolica do Parana (Brazil)
Robert Sabourin, Ecole de Technologie Superieure/Montreal (Canada)


Published in SPIE Proceedings Vol. 3030:
Applications of Artificial Neural Networks in Image Processing II
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

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