Segmentation and Edge Extraction of Grayscale Images Using Firefly and Artificial Bee Colony Algorithms

Abstract

This chapter proposes an unsupervised grayscale image segmentation method based on the Firefly and Artificial Bee Colony algorithms. The Firefly Algorithm is applied in a histogram-based research of cluster centroids to determine the number of clusters and the gray levels, successively used in the initialization step for the parameter estimation of a Gaussian Mixture Model. The coefficients of the linear super-position of Gaussians can be thought of as prior probabilities of each component. Applying the Bayes rule, the posterior probabilities of the grayscale intensities are evaluated and their maxima are used to assign each pixel to clusters. Subsequently, region spatial information is extracted to form homogeneous regions through ABC algorithm. Initially, scout bees are moving on the search space describing random paths, with food sources given by the detected homogeneous regions. Then onlooker bees rush to scouts' aid proportionally to unclassified pixels enclosed into the bounded boxes of the discovered regions.