Rr-cr-IJADE: An efficient differential evolution algorithm for multilevel image thresholding |
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| Affiliation: | 1. Laboratório de Inteligência Computacional do Araripe, Instituto Federal do Sertão Parnambucano, Brazil;2. Centro de Informática, Universidade Federal de Pernambuco, Brazil;1. Universidad de Alcalá, 28871 Alcalá de Henares Madrid, Spain;2. Universidade de Santiago de Compostela, Santiago de Compostela Galicia E-15782, Spain;1. Guangdong Provincial Key Lab. of Computer Integrated Manufacturing Systems, School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, Guangdong 510006, China;2. Knowledge Management and Innovation Research Centre, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong 999077, China;1. Department of Biomedical Engineering, National Institute of Technology, Raipur, Chhattisgarh, India;2. Department of Computer Applications, National Institute of Technology, Raipur, Chhattisgarh, India;3. Department of Electrical Engineering, National Institute of Technology, Raipur, Chhattisgarh, India;1. Departamento de Ciencias de la Ingeniería, Facultad de Ingeniería, Universidad Andres Bello, Antonio Varas 880, Santiago, Chile;2. Yahoo! Research Latin America, Blanco Encalada 2120, Santiago, Chile |
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| Abstract: | 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. |
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