Abstract
Thresholding is a simple and most commonly used method for image segmentation. It’s known that the minimum cross entropy thresholding (MCET) has been widely used in image threshold selection. The bat algorithm (BA) come from the social behavior of the swarm of bats, and it’s one of the popular techniques for optimization. This paper proposed an improved BA (IBA) by using time-varying inertia weights into the update formula, and six benchmark functions were selected for the simulation test. Then, the IBA was used for searching the optimal MCET thresholds. What’s more, three different methods that the improved particle swarm optimization (IPSO), the fuzzy-clustering method (FC) and basic BA are carried out for comparison with the proposed algorithm. The results demonstrate that the proposed IBA can obtain more fast and stable results.
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Acknowledgment
The authors would like to thank the Natural Science Basic Research Plan in Shaanxi province of China No. 2015JM6296 for support of this work.
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Chen, S., Peng, GH. (2019). Multilevel Minimum Cross Entropy Threshold Selection Based on the Improved Bat Optimization. In: Xhafa, F., Patnaik, S., Tavana, M. (eds) Advances in Intelligent, Interactive Systems and Applications. IISA 2018. Advances in Intelligent Systems and Computing, vol 885. Springer, Cham. https://doi.org/10.1007/978-3-030-02804-6_42
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DOI: https://doi.org/10.1007/978-3-030-02804-6_42
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