Abstract
For the traditional multi-thresholds segmentation algorithms, usually it would take too much time in finding the optimal solution. As one of the widely used swarm-intelligence optimization algorithms, ant colony optimization (ACO) algorithm has been introduced to optimize the thresholding search process. The traditional ACO is improved in this paper to get a faster convergence speed and applied in Otsu multi-thresholds segmentation algorithms. When the ant colony is initialized, each member of the ant colony is distributed evenly in the solution space, so that it could search the entire solution space as fast as possible. In the search process, the random step length of ants moving is generated by the Lévy flight pattern, but the global transition probability of the traditional ACO is used to control the search range of the ant colony. The experimental results show that the proposed algorithm could obtain the optimal thresholds faster and more effectively than the traditional Otsu algorithm and the Otsu based on traditional ACO.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China (61672259, 61602203), and Outstanding Young Talent Foundation of Jilin Province (20170520064JH).
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Qin, J., Shen, X., Mei, F. et al. An Otsu multi-thresholds segmentation algorithm based on improved ACO. J Supercomput 75, 955–967 (2019). https://doi.org/10.1007/s11227-018-2622-0
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DOI: https://doi.org/10.1007/s11227-018-2622-0