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An adaptive firefly algorithm for multilevel image thresholding based on minimum cross-entropy

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Abstract

Multilevel thresholding image segmentation has attracted a lot of attention in the last several years since it has plenty of applications. The traditional exhaustive search methods are efficient for bi-level thresholding. However, they are time-consuming when extended to multilevel thresholding. To tackle this problem, a novel adaptive firefly algorithm (AFA) for multilevel thresholding using the minimum cross-entropy as its objective function has been proposed in this paper. The performance of the proposed algorithm has been examined on a set of benchmark images using various numbers of thresholds and has been compared with five different firefly variant algorithms. The experimental results indicated that the proposed algorithm outperformed the other five algorithms in terms of image segmentation quality, accuracy, and computation time.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (31671591); Guangdong Provincial Special Fund For Modern Agriculture Industry Technology Innovation Teams (2021KJ108); Natural Science Foundation of Guangdong Province of China (2020A1515010784); China Agriculture Research System of MOF and MARA (CARS-26) and Guangdong Youth Characteristic Innovation Project (2021KQNCX120).

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  1. South China Agricultural University, Guangzhou, China

    Yi Wang & Shuran Song

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  1. Yi Wang
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  2. Shuran Song
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Correspondence to Shuran Song.

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Wang, Y., Song, S. An adaptive firefly algorithm for multilevel image thresholding based on minimum cross-entropy. J Supercomput 78, 11580–11600 (2022). https://doi.org/10.1007/s11227-021-04281-7

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