A Hybrid Improved Group Search Optimization and Otsu Method for Color Image Segmentation

Image segmentation is a fundamental process for image analysis and computer vision. One of the most popular image segmentation methods is Otsu algorithm, originally proposed to segment a grayscale image in two classes, but extended to multi-level thresholding afterwards. Although effective, the computational cost for multi-level Otsu limits its application in real world problems, and, recently, many evolutionary algorithms (EAs) have been applied to optimize Otsu algorithm. In this paper, a hybrid Otsu and improved Group Search Optimization (GSO) algorithm is presented to deal with multi-level color image thresholding problem, named IGSO. IGSO implements a weedout operator to prune the worst members from GSO population. We also evaluate the influence of two treatments to deal with outbounded individuals from EAs population. The proposed IGSO is compared to other EAs from literature through twelve real color image problems, showing its potential and robustness even when compared to original GSO algorithm.