A hybrid bio-inspired learning algorithm for image segmentation using multilevel thresholding

In the field of image analysis, segmentation is one of the most important preprocessing steps. One way to achieve segmentation is the use of threshold selection, where each pixel that belongs to a determined class, based on the mutual visual characteristics, is labeled according to the selected threshold. In this work, a combination of two pioneer methods, namely Otsu and Kapur, are investigated to solve the threshold selection problem. Optimum parameters of these objective functions are calculated using Bacterial Foraging (BF) optimization algorithm, for its accuracy, and Harmony Search (HS), for its speed. However, the biggest problem of soft computing family algorithms is catching into a local optimum. To resolve this critical issue, we investigate the power of Learning Automata (LA) which works as a controller to make switching between these two optimization methods. LA is a heuristic method which can solve complex optimization problems with interesting results in parameter estimation. Despite other techniques commonly seek through the parameter map, LA explores in the probability space, providing appropriate convergence properties and robustness. The proposed method is tested on benchmark images and shows fast convergence avoiding the typical sensitivity to initial conditions such as the Expectation-Maximization (EM) algorithm or the complex, and time-consuming computations which are commonly found in gradient methods. Experimental results demonstrate the algorithm’s ability to perform automatic multi-threshold selection and show interesting advantages as it is compared to other algorithms solving the same task.