基于改进犹豫模糊C-均值的图像分割

犹豫模糊C-均值(hesitant fuzzy C-means, HFCM)聚类算法在一定程度上处理了图像中不同像素块之间的不确定性, 但由于其目标函数中不包含任何局部空间信息, 因此对噪声比较敏感, 当噪声较大时无法获得较好的分割精度. 针对上述问题, 提出了一种改进犹豫模糊C-均值(improved hesitant fuzzy C-means, IHFCM)的图像分割方法. 首先给出了犹豫模糊元(hesitant fuzzy element)的补齐方法, 然后提出了犹豫模糊元之间的相似性度量, 利用犹豫模糊元之间的相似性度量构造了新颖的模糊因子融合到HFCM的目标函数中, 新的模糊因子不仅考虑了局部窗口中的空间信息而且考虑了像素间的相似性, 平衡噪声带来的影响且保留了图像细节. 最后, 在合成图像、BSDS500数据集图像以及自然图像上的分割实验结果表明, 所提出的IHFCM算法对噪声有良好的鲁棒性, 提升了分割精度.

Image Segmentation Based on Improved Hesitant Fuzzy C-means

The hesitant fuzzy C-means (HFCM) clustering algorithm has addressed the uncertainty between different pixel blocks in an image to some extent. However, as its objective function does not contain any local information, it is very sensitive to noise and cannot achieve good segmentation accuracy when the noise is large. This study proposes an image segmentation method based on improved HFCM (IHFCM) to address the above issues. Firstly, the completion method of hesitant fuzzy elements is given, and then a similarity measure between hesitant fuzzy elements is defined. Using the defined similarity measure, the study constructs a novel fuzzy factor and fuses it into the objective function of HFCM. The new fuzzy factor considers not only spatial information in the local window but also the similarity between pixels, balancing the impact of noise while preserving image details. Finally, experimental results on synthesized images, BSDS500 dataset images, and natural images show that the proposed IHFCM algorithm has good robustness to noise and improves segmentation accuracy.