Unsupervised classification of grayscale image using Probabilistic Neural Network (PNN)
Jawad Iounousse, Ahmed Farhi, Ahmed El Motassadeq, Hassan Chehouani, Salah Er‐Raki · 2012
Image classification is a very common step in image analysis process. It is a low-level processing that precedes the step of measuring, understanding and decision. Its purpose is image partitioning into related and homogeneous regions in the sense of a homogeneity criterion. In this paper, we proposed a procedure to determine the optimal number of classes in a grayscale image classification based on a Probabilistic Neural Network (PNN). The used procedure is completely automatic with no parameter adjusting. The results on synthetic images show a high robustness and better performance. The results showed that PNN is a good technique for one-dimensional data classifying.