OptiMUSIG: An Optimized Gray Level Image Segmentor

摘要

A multilevel sigmoidal (MUSIG) activation function is efficient in segmenting multilevel images. The function uses equal and fixed class responses, assuming the homogeneity of image information content. In this article, a novel approach for generating optimized class responses of the MUSIG activation function, is proposed. Three different types of objective function are used to measure the quality of the segmentation in the proposed genetic algorithm based optimization method. Results of segmentation of two real life images by the optimized MUSIG (OptiMUSIG) activation function with optimized class responses show better performances over the MUSIG activation function with equal and fixed responses.