Abstract
In this paper, an adaptive multi-level thresholding segmentation based on multi-objective evolutionary algorithm (AMT_ME) is proposed. Firstly, the between-class variances and the entropy criteria are utilized as the multi-objective fitness functions. Then the threshold-based encoding technique, variable length sub-populations, crowed binary tournament selection, hybrid crossover operation, mutation and non-dominated sorting are produced in this article. Finally, the weight ratio of intra-class variance and between-class variance is used to obtain the optimum number of the thresholds and the optimal thresholds. The experimental results demonstrate good performance of the AMT_ME in solving thresholding segmentation problem by compared with ATMO, Otsu’s and Kapur’s methods.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China (Grant Nos. 61571361, 61102095, and 61202153), the Science and Technology Plan in Shaanxi Province of China (Grant No. 2014KJXX-72), and the Fundamental Research Funds for the Central Universities (Grant No. GK201503063).
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Zheng, Y., Zhao, F., Liu, H., Wang, J. (2016). Adaptive Multi-level Thresholding Segmentation Based on Multi-objective Evolutionary Algorithm. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9712. Springer, Cham. https://doi.org/10.1007/978-3-319-41000-5_61
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DOI: https://doi.org/10.1007/978-3-319-41000-5_61
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