基于改进像素相关性模型的图像分割算法

Image segmentation algorithm based on improved pixel correlation model

图像分割是计算机视觉中的研究热点和难点。基于局部信息的模糊聚类算法(FLICM)在一定程度上提升了模糊聚类算法的鲁棒性,但噪声强度较大时无法获得较好的图像分割效果。针对传统的模糊聚类算法分割精度不佳等问题,提出了改进像素相关性模型的图像分割算法。首先通过分析像素的局部统计特征,设计了一种新型的像素相关性模型,在此基础上,有效利用非局部信息挖掘图像中的细节,提升图像分割效果。实验采用多种评价指标进行分割结果的评估,并与多种模糊聚类系列算法进行对比。在合成图像、自然图像、医学图像和遥感图像上的实验表明,基于改进像素相关性的模糊聚类算法可以有效平衡对噪声的抵抗程度和对图像细节信息的保留程度,分割效果和鲁棒性优于相关算法。

Image segmentation is the research hotspot and difficulty in computer vision. Based on local information, the fuzzy local information C-means(FLICM) clustering algorithm improves the robustness of the algorithm to a certain extent, but cannot attain the expected image segmentation effect in the case of high noise intensity. Aiming at the low segmentation accuracy of traditional fuzzy clustering algorithm, an improved image segmentation algorithm based on pixel correlation model was proposed. Firstly, a new pixel correlation model was designed by analyzing the local statistical characteristics of pixels. On this basis, non-local information was effectively employed to mine the details in the image and improve the image segmentation effect. In the experiment, a variety of evaluation indexes were used to evaluate the segmentation results, and compared with a variety of common fuzzy clustering algorithms. Experimental results show that the fuzzy clustering algorithm based on improved pixel correlation can effectively balance the degree of resistance to noise and the degree of retention of image details in synthetic images, natural images, medical images, and remote sensing images, and that the segmentation effect and robustness are superior to the correlation algorithm.