Multi-objective Cross Section Projection Otsu's Method Based on Memory Knetic-molecular Theory Optimization Algorithm

XIAO Leyi; OUYANG Honglin; FAN Chaodong

doi:10.11999/JEIT170301

Volume 40 Issue 1

Jan. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(1): 189-199

XIAO Leyi, OUYANG Honglin, FAN Chaodong. Multi-objective Cross Section Projection Otsu's Method Based on Memory Knetic-molecular Theory Optimization Algorithm[J]. Journal of Electronics & Information Technology, 2018, 40(1): 189-199. doi: 10.11999/JEIT170301

Citation:

XIAO Leyi, OUYANG Honglin, FAN Chaodong. Multi-objective Cross Section Projection Otsu's Method Based on Memory Knetic-molecular Theory Optimization Algorithm[J]. Journal of Electronics & Information Technology, 2018, 40(1): 189-199. doi: 10.11999/JEIT170301

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Multi-objective Cross Section Projection Otsu's Method Based on Memory Knetic-molecular Theory Optimization Algorithm

doi: 10.11999/JEIT170301

1.
(College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)
2.
(College of Information Engineering, Xiangtan University, Xiangtan 411105, China)

Funds:

The National Natural Science Foundation of China (61573299), The Natural Science Foundation of Hunan Province (2016JJ3125), The Foundation of Hunan Educational Committee (15C1327)

Received Date: 2017-04-05
Rev Recd Date: 2017-08-28
Publish Date: 2018-01-19

Abstract

Abstract

The threshold value of Q in the post process of traditional cross section projection Otsus method is a constant, which is not universal applicability for images with different noises. To solve this problem, this paper proposes a multi-objective cross section projection Otsu's method based on memory knetic-molecular theory ptimization algorithm. Based on the maximum between-class variance criterion and the maximum Peak Signal to Noise Ratio (PSNR) criterion, a multi-objective image segmentation model is established to take into account the segmentation accuracy and anti-noise capability for image segmentation by combining threshold Q with segmentation threshold T. In order to improve the efficiency of the algorithm, a memory knetic-molecular theory optimization algorithm is proposed for the multi-objective cross section projection Otsu's method by introducing the artificial memory principles into knetic-molecular theory optimization algorithm. The experimental results show that this method has significant advantages in segmentation accuracy, anti-noise capability and robustness, and is more universal applicability for images with different noises.
- Image segmentation,
- Maximum between-class variance,
- Multi-objective optimization,
- Kinetic- molecular theory optimization algorithm,
- Memory principles

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References(33)

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