Automatic Segmentation by Using an Algorithm Based on the Behavior of Locust Swarms

Abstract

As an alternative to classical techniques, the problem of image segmentation has also been handled through evolutionary methods. Recently, several algorithms based on evolutionary principles have been successfully applied to image segmentation with interesting performances. However, most of them maintain two important limitations: (1) they frequently obtain sub-optimal results (misclassifications) as a consequence of an inappropriate balance between exploration and exploitation in their search strategies; (2) the number of classes is fixed and known in advance. This chapter presents an algorithm for the automatic selection of pixel classes for image segmentation. The presented method combines a recent evolutionary method with the definition of a new objective function that appropriately evaluates the segmentation quality with respect to the number of classes. The evolutionary algorithm, called Locust Search (LS), is based on the behavior of swarms of locusts. Different to the most of existent evolutionary algorithms, it explicitly avoids the concentration of individuals in the best positions, avoiding critical flaws such as the premature convergence to sub-optimal solutions and the limited exploration-exploitation balance. Experimental tests over several benchmark functions and images validate the efficiency of the presented technique with regard to accuracy and robustness.