Color Image Segmentation by NSGA-II Based ParaOptiMUSIG Activation Function

Based on different criteria any real life problem generates a set of alternative solutions instead of a single optimal solution. Color image segmentation by single objective based parallel optimized MUSIG (ParaOptiMUSIG) activation function may or may not render better solutions for different objective functions. To overcome this problem, a non-dominated sorting genetic algorithm-II (NSGA-II) based ParaOptiMUSIG activation function is proposed in this article to segment color images. Segmentation is achieved using optimized class responses from the image content with a parallel self organizing neural network (PSONN) architecture. Some standard objective functions which are used to assess the quality of the segmented images forms the NSGA-II based image segmentation method.