Fast Algorithm of Image Selective Segmentation Model Based on Convex Relaxation

JIN Zhengmeng; LIAN Xiaoyu; YANG Tianji

doi:10.11999/JEIT210184

Volume 44 Issue 7

Jul. 2022

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JIN Zhengmeng, LIAN Xiaoyu, YANG Tianji. Fast Algorithm of Image Selective Segmentation Model Based on Convex Relaxation[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2522-2530. doi: 10.11999/JEIT210184

Citation:

JIN Zhengmeng, LIAN Xiaoyu, YANG Tianji. Fast Algorithm of Image Selective Segmentation Model Based on Convex Relaxation[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2522-2530. doi: 10.11999/JEIT210184

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Fast Algorithm of Image Selective Segmentation Model Based on Convex Relaxation

doi: 10.11999/JEIT210184

Nanjing University of Posts and Telecommunications, Nanjing 210023, China

Funds: The National Natural Science Foundation of China (11671004, 11771005)

Received Date: 2021-03-03
Accepted Date: 2022-03-07
Rev Recd Date: 2022-03-07

Available Online: 2022-03-18

Publish Date: 2022-07-25

Abstract

Abstract

In view of the non-convex defect of the image selective segmentation model based on geodesic distance, a convex selective segmentation model by convex relaxation is proposed. The relationship between the solution of the convex model and that of the original model is given. Then by incorporating the Alternating Direction Method of Multipliers (ADMM), this paper designs a fast algorithm for numerically solving the convex model and gives the convergence of the algorithm. Finally, numerical experimental results show that convergence speed of the proposed algorithm is much better than the original algorithm based on the additive operator splitting method, and the segmentation results of the proposed algorithm are more accurate.
- Image selective segmentation,
- Convex relaxation,
- Alternate minimization,
- Alternating Direction Method of Multipliers (ADMM)

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References(16)

References

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