Minimum Spanning Tree Segmentation and Extract with Image Edge Weight Optimization

LIN Jianpu; WANG Dong; XIAO Zhiyang; LIN Zhixian; ZHANG Yong’ai

doi:10.11999/JEIT220182

Volume 45 Issue 4

Apr. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(4): 1494-1504

LIN Jianpu, WANG Dong, XIAO Zhiyang, LIN Zhixian, ZHANG Yong’ai. Minimum Spanning Tree Segmentation and Extract with Image Edge Weight Optimization[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1494-1504. doi: 10.11999/JEIT220182

Citation:

LIN Jianpu, WANG Dong, XIAO Zhiyang, LIN Zhixian, ZHANG Yong’ai. Minimum Spanning Tree Segmentation and Extract with Image Edge Weight Optimization[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1494-1504. doi: 10.11999/JEIT220182

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Minimum Spanning Tree Segmentation and Extract with Image Edge Weight Optimization

doi: 10.11999/JEIT220182

School of Advanced Manufacturing, Fuzhou University, Quanzhou 362200, China

Funds: The National Key R&D Program of China (2021YFB3600603), The Natural Science Foundation of Fujian Province (2019J01221, 2020J01468)

Received Date: 2022-02-25
Accepted Date: 2022-06-08
Rev Recd Date: 2022-05-13

Available Online: 2022-06-13

Publish Date: 2023-04-10

Abstract

Abstract

The unsupervised image segmentation method is sensitive to noise, leading to difficult building image model and poor accuracy of segmentation results. In this paper, a minimum spanning tree segmentation and extract with image edge weight optimization is proposed. Firstly, L₀ gradient minimum is used to smooth the noise. The Canny edge detection with Otsu is optimized to obtain more accurate edge information. Secondly, the weight function is redesigned and the weighted graph by using more reasonable color difference space is constructed. The segmentation criterion is improved to optimize the process of object merging and distinguishing. Finally, different types of images are chosen to conduct experiments with noise resistance and segmentation effect. Experimental comparing results show that the proposed algorithm has excellent anti-noise performance, and the segmentation accuracy is improved by 5.15% on average, the over-segmentation rate is decreased by 32.07% on average, and the under-segmentation rate is decreased by 2.69% on average. Moreover, this method is applied to the river and lake extraction of aviation and remote sensing images, and the result has more complete structure, less irrelevant information and better anti-noise performance.
- Image segmentation,
- Image edge,
- Minimum spanning tree,
- Image extraction,
- Airborne remote sensing

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References(23)

References

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