Binary image compression using identity-mapping backpropagation neural network

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 x 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 any degradation and, yet, with a good reduction in the number of input neurons.