Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapur's, Otsu and Tsallis functions

In this paper, a modified artificial bee colony (MABC) algorithm based satellite image segmentation using different objective function has been presented to find the optimal multilevel thresholds. Three different methods are compared with this proposed method such as ABC, particle swarm optimization (PSO) and genetic algorithm (GA) using Kapur's, Otsu and Tsallis objective function for optimal multilevel thresholding. The experimental results demonstrate that the proposed MABC algorithm based segmentation can efficiently and accurately search multilevel thresholds, which are very close to optimal ones examined by the exhaustive search method. In MABC algorithm, an improved solution search equation is used which is based on the bee's search only around the best solution of previous iteration to improve exploitation. In addition, to improve global convergence when generating initial population, both chaotic system and opposition-based learning method are employed. Compared to other thresholding methods, segmentation results of the proposed MABC algorithm is most promising, and the computational time is also minimized.