基于加权滤波与核度量的鲁棒图像分割算法
A Robust Image Segmentation Algorithm Based on Weighted Filtering and Kernel Metric
刘以 1张小峰 2孙玉娟 1王桦 1张彩明3
作者信息
- 1. 鲁东大学信息与电气工程学院,山东 烟台 264025
- 2. 鲁东大学信息与电气工程学院,山东 烟台 264025;烟台理工学院信息工程学院,山东 烟台 264003
- 3. 山东大学软件学院,山东 济南 250014
- 折叠
摘要
图像分割是计算机视觉领域的重要研究方向.模糊聚类方法由于其无监督的特性,在图像分割中得到了广泛的应用.然而,传统的模糊聚类方法在处理含高强度噪声和复杂形状的图像时,往往分割效果不理想.为了解决这一问题,提出了一种基于显著性检测的权重因子,用于构建加权滤波器和像素相关性模型,从而提高算法的抗噪能力.所提加权滤波器在结构相似性上比传统滤波器的最优结果高出0.1.此外,引入核度量以适应复杂图像的分割需求.在合成图像、自然图像、遥感图像和医学图像上进行了大量实验,结果表明,所提算法在视觉效果上优于传统方法,并且在分割精度上比传统方法的最优结果高出2%.
Abstract
Image segmentation is an important research direction in computer vision.Fuzzy clustering methods have been widely applied in image segmentation due to their unsupervised nature.However,traditional fuzzy clustering methods often fail to segment images with high-intensity noise and complex shapes.To solve this problem,a weighted factor is proposed based on saliency detection to construct a weighted filter and a pixel correlation model,which improves the noise resistance of the algorithm.The proposed weighted filter outperforms the optimal results of the traditional filter in terms of structural similarity by 0.1.Moreover,a kernel metric is introduced to accommodate the segmentation needs of complex images.Extensive experimental results on synthetic,natural,remote sensing and medical images demonstrate that the proposed algorithm outperforms the traditional methods in visual effects and improves the segmentation accuracy by 2%compared with the optimal results of traditional methods.
关键词
图像分割/模糊聚类/加权滤波/核度量/像素相关性Key words
image segmentation/fuzzy clustering/weighted filtering/kernel metric/pixel correlation引用本文复制引用
基金项目
国家自然科学基金(62007017)
国家自然科学基金(U22A2033)
国家自然科学基金(61873117)
国家自然科学基金(62171209)
国家自然科学基金(62176140)
出版年
2024
NETL
NSTL
万方数据