Modified Self Organizing Feature Map Neural Network (MSOFM NN) Based Gray Image Segmentation

Abstract

Abstract Image segmentation is the most important and crucial part of image analysis system. The accuracy of any image analysis system is highly dependent on the accuracy of image segmentation. In this paper, we propose an image segmentation method that is based on Modified Self Organizing Feature Map. The proposed method classifies image pixels based on their intensity values for image segmentation. In proposed method, there is no burden of feature extraction and training data set. First the image is converted into one dimensional vector (input vector) and image pixel intensity values directly feed to the input layer of SOFM NN. To classify each pixel class, we modified the SOFM NN by adding an extra layer of neurons. This extra layer does not contribute in the process of weight updation. After the termination of self organizing network and weights updation process, this extra layer of neurons checks for input intensities to closest match and credited that input intensity to concerned class. The proposed algorithm is tested on the publicly available Berkeley Segmentation Data set. The results clearly show that the algorithm present in this paper is better than existing algorithms