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Rr-cr-IJADE: An efficient differential evolution algorithm for multilevel image thresholding
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DOI：10.1016/j.eswa.2017.08.029.png)
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Abstract
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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 R-cr-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. (C) 2017 Elsevier Ltd. All rights reserved.
Keywords:
Multilevel thresholding
Otsu's function
Evolutionary and optimization algorithm
Differential evolution
Mutation strategy
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