True color image segmentation by an optimized multilevel activation function

A novel neuro-fuzzy-genetic approach is presented in this article to segment a true color image into different color levels. A MUSIG activation function induces multiscaling capabilities in a parallel self organizing neural network (PSONN) architecture. The function however resorts to equal and fixed class responses, assuming the homogeneity of image information content. In the proposed approach, genetic algorithm has been used to generate optimized class responses of the MUSIG activation function. Subsequently, the color images are segmented by applying the resultant optimized multilevel sigmoidal (OptiMUSIG) activation function. Comparative results of segmentation of two real life true color images indicate better segmentation efficiency of the OptiMUSIG activation function over the standard MUSIG activation function.