A study of chaotic maps in differential evolution applied to gray-level image thresholding

Image segmentation is an important preprocessing step in many computer vision applications, using the image thresholding as one of the simplest and the most applied methods. Since the optimal thresholds' selection can be regarded as an optimization problem, it can be found easily by applying any meta-heuristic with an appropriate objective function. This paper investigates the impact of different chaotic maps, embedded into a self-adaptive differential evolution for the purpose of image thresholding. The Kapur entropy is used as an objective function that maximizes the entropy of different regions in the image. Three chaotic maps, namely the Kent, Logistic and Tent, found commonly in literature, are studied in this paper. The applied chaotic maps are compared to the original differential evolution, self-adaptive differential evolution, and the state-of-the-art L-Shade tested on four images. The results show that the applied chaotic maps improve the results obtained using the traditional randomized method.