Rr-cr-IJADE: An efficient differential evolution algorithm for multilevel image thresholding

• Published in: Expert systems with applications Vol. 90; pp. 272 - 289
• Main Authors: Muangkote, Nipotepat, Sunat, Khamron, Chiewchanwattana, Sirapat
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 30.12.2017; Elsevier BV
• Subjects: Algorithms; Differential evolution; Evolutionary algorithms; Evolutionary and optimization algorithm; Expert systems; Heuristic methods; Image segmentation; Multilevel; Multilevel thresholding; Mutation strategy; Otsu's function; Satellite imagery; State of the art; Swarm intelligence; Thresholds; Differential evolution; Multilevel thresholding; Otsu's function; Mutation strategy; Evolutionary and optimization algorithm
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2017.08.029

•The “DE/rand-to-rank/1” scheme was proposed.•The Rr-cr-IJADE algorithm was also proposed.•Otsu's function is the objective function for multilevel thresholding.•The experiments were conducted on 2 to 16 and 24 to 64 thresholds.•The Rr-cr-IJADE was ranked first (1st) in all experiments. There is a need for a new method of segmentation to improve the efficiency of expert systems that need segmentation. Multilevel thresholding is a widely used technique that uses threshold values for image segmentation. However, from a computational stand point, the search for optimal threshold values presents a challenging task, especially when the number of thresholds is high. To get the optimal threshold values, a meta-heuristic or optimization algorithm is required. Our proposed algorithm is referred to as Rr-cr-IJADE, which is an improved version of Rcr-IJADE. Rr-cr-IJADE uses a newly proposed mutation strategy, “DE/rand-to-rank/1”, to improve the search success rate. The strategy uses the parameter F adaptation, crossover rate repairing, and the direction from a randomly selected individual to a ranking-based leader. The complexity of the proposed algorithm does not increase, compared to its ancestor. The performance of Rr-cr-IJADE, using Otsu's function as the objective function, was evaluated and compared with other state-of-the-art evolutionary algorithms (EAs) and swarm intelligence algorithms (SIs), under both ‘low-level’ and ‘high-level’ experimental sets. Within the ‘low-level’ sets, the number of thresholds varied from 2 to 16, within 20 real images. For the ‘high-level’ sets, the threshold numbers chosen were 24, 32, 40, 48, 56 and 64, within 2 synthetic pseudo images, 7 satellite images, and three real images taken from the set of 20 real images. The proposed Rr-cr-IJADE achieved higher success rates with lower threshold value distortion (TVD) than the other state-of-the-art EA and SI algorithms.