2-Layer Perceptron Performance Improvement in Classifying 26 Turned Monochrome 60-by-80-Images via Training with Pixel-Distorted Turned Images

Authors

  • Вадим Васильович Романюк Khmelnitskiy national university,

DOI:

https://doi.org/10.20535/1810-0546.2014.5.35234

Keywords:

Automatization, Object classification, Neocognitron, Perceptron, Monochrome images, Pixel-distortion, Rotation, Turn-distortion, Training set, Classification error percentage

Abstract

There is tried 2-layer perceptron in classifying turn-distorted objects at acceptable classification error percentage. The object model is a letter of English alphabet, which is monochrome 60-by-80-image. Neither training 2-layer perceptron with pixel-distorted images, nor with turn-distorted images makes it classify satisfactorily. Therefore in classifying turn-distorted images a 2-layer perceptron performance might be improved via training under distortion modification. The modified distorted images for the training set are suggested as mixture of turn-distorted and pixel-distorted images. Thus the training set is formed of pixel-distorted turned images on the 26 alphabet letters pattern. A performance improvement is revealed when there are passed much more training samples through 2-layer perceptron. This certainly increases traintime, but instead 2-layer perceptron can classify either of pixel-distorted images and pixel-distorted turned images. At that the trained 2-layer perceptron is about 35 times faster than neocognitron in classifying objects of the considered medium format.

Author Biography

Вадим Васильович Романюк, Khmelnitskiy national university

Doctor of sciences (engineering), associate professor, professor at the Khmelnitskiy national university

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Published

2014-11-19

Issue

No. 5 (2014): Engineering

Section

Art

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