Image Binarization Using Multi-Layer Perceptron: A Semi-Supervised Approach

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.