Image Binarization Using Multi-Layer Perceptron: A Semi-Supervised Approach
Research Area: | Volume 1 Issue 2, March 2012 | Year: | 2012 |
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Type of Publication: | Article | Keywords: | Backpropagation Algorithm, Image Binarization, K-means clustering, Semi-supervised learning |
Authors: |
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Journal: | IJEIR | Volume: | 1 |
Number: | 2 | Pages: | 134-139 |
Month: | March | ||
ISSN: | 2277-5668 | ||
Abstract: | In this paper, we have discussed the Image
Binarization technique using Multilayer Perceptron (MLP).
The purpose of Image Binarization is to extract the lightness
(brightness, density) as a feature amount from the Image. It
converts a gray-scale image of up to 256 gray levels to a black
and white image. We use Backpropagation algorithm for
training MLP. It is a supervised learning technique. Here Kmeans
clustering algorithm has been used for clustering a
256 × 256 gray-level image. The dataset obtained by this is
fed to the MLP and processed in a Semi-Supervised way
where some training samples are taken as Known patterns
(for training) and others as Unknown patterns. Finally
through this approach a Binarized image is produced. |
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