Multi-threshold Image Segmentation of 2D Otsu Based on Improved Adaptive Differential Evolution Algorithm

Jun LUO; Yongsong YANG; Baoyu SHI

doi:10.11999/JEIT180949

Volume 41 Issue 8

Aug. 2019

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Jun LUO, Yongsong YANG, Baoyu SHI. Multi-threshold Image Segmentation of 2D Otsu Based on Improved Adaptive Differential Evolution Algorithm[J]. Journal of Electronics & Information Technology, 2019, 41(8): 2017-2024. doi: 10.11999/JEIT180949

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Jun LUO, Yongsong YANG, Baoyu SHI. Multi-threshold Image Segmentation of 2D Otsu Based on Improved Adaptive Differential Evolution Algorithm[J]. Journal of Electronics & Information Technology, 2019, 41(8): 2017-2024. doi: 10.11999/JEIT180949

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Multi-threshold Image Segmentation of 2D Otsu Based on Improved Adaptive Differential Evolution Algorithm

doi: 10.11999/JEIT180949

Key Laboratory of Optoelectronic Technology and System of Ministry of Education, Chongqing University, Chongqing 400030, China

Received Date: 2018-10-12
Rev Recd Date: 2019-03-04

Available Online: 2019-03-28

Publish Date: 2019-08-01

Abstract

Abstract

The multi-threshold image segmentation of the classical 2D maximal between-cluster variance method has deficiencies such as large computation, long calculation time, low segmentation precision and so on. A multi-threshold segmentation of 2D Otsu based on improved Adaptive Differential Evolution (JADE) algorithm is proposed. Firstly, in order to enhance the quality of the initialized population and improve the adaptability of the control parameters, the chaotic mapping mechanism is integrated into the JADE algorithm. Furthermore, the optimal segmentation threshold of 2D Otsu multi-threshold image is solved by improved JADE algorithm. Finally, the algorithm is compared with multi-threshold image segmentation method of 2D Otsu based on Differential Evolution (DE), JADE, Improved Differential Evolution with Adaptive Sinusoidal Parameters (LSHADE-cnEpSin) and Enhanced Adaptive Differential Transformation Differential Evolution (EFADE) algorithm. The experimental results show that compared with the other four algorithms, the multi-threshold image segmentation of 2D Otsu based on the improved JADE algorithm has a significant improvement in terms of segmentation speed and accuracy.
- Image segmentation,
- Maximum interclass variance method,
- Chaotic map,
- Improved Adaptive Differential Evolution (JADE) algorithm

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References

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