Image Edge Detection Using Ant Colony Optimization with Genetic Algorithm

Abstract

This paper presents Ant Colony Optimization (ACO) along with genetic algorithm-based optimization technique for edge detection. The problem of edge detection is formulated as one of choosing a minimum cost edge configuration. ACO can be used to find good solutions to combinatorial optimization problems that can be transformed into the problem of finding good paths through a weighted construction graph. Similarly, the genetic algorithm views edge configurations as two-dimensional chromosomes with fitness values inversely proportional to their costs. The design of the crossover and the mutation operators in the context of the twodimensional chromosomal representation is described. In this paper, an edge detection technique that is based on ACO and genetic algorithm is presented. The proposed method establishes a pheromone matrix that represents the edge information at each pixel based on the routes formed by the ants dispatched on the image. The movement of the ants is guided by the local variation in the image’s intensity values. The proposed ACO-based edge detection method takes advantage of the improvements introduced in ant colony system, one of the main extensions to the original ant system.
In genetic algorithm, the design of the crossover and the mutation operators in the context of the two-dimensional chromosomal representation is described. The knowledgeaugmented mutation operator which exploits knowledge of the local edge structure is shown to result in rapid convergence. The incorporation of meta-level operators and strategies such as the elitism strategy and various combinations of meta-level operators can be tested on synthetic and natural images.