基于记忆分子动理论优化算法的多目标截面投影Otsu图像分割

肖乐意; 欧阳红林; 范朝冬

doi:10.11999/JEIT170301

基于记忆分子动理论优化算法的多目标截面投影Otsu图像分割

doi: 10.11999/JEIT170301

1.
(湖南大学电气与信息工程学院长沙 410082) ②(湘潭大学信息工程学院湘潭 411105)

基金项目:

国家自然科学基金(61573299)，湖南省自然科学基金(2016JJ3125)，湖南省教育厅科学研究项目(15C1327)

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- 文章访问数: 1454
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- 被引次数: 0
出版历程
- 收稿日期: 2017-04-05
- 修回日期: 2017-08-28
- 刊出日期: 2018-01-19

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

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)

摘要

摘要: 传统截面投影Otsu法后处理过程中的阈值Q为预先设定的常量，对含噪程度不同的图像普适性较差。该文提出一种基于记忆分子动理论优化算法的多目标截面投影Otsu法。该方法将阈值Q作为变量，结合分割阈值T，基于最大类间方差和最大峰值信噪比准则建立多目标图像分割模型，以兼顾图像分割的准确性和抗噪性；为免阈值增加而影响算法效率，将人工记忆原理引入分子动理论优化算法，设计了一种基于记忆分子动理论优化算法的多目标图像分割模型求解方法。实验表明：该方法分割准确、抗噪性强、鲁棒性好，对含不同噪声的图像更具普适性。
- 图像分割 /
- 最大类间方差 /
- 多目标优化 /
- 分子动理论优化算法 /
- 记忆原理
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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