基于球簇聚类的超像素分割迭代算法

Superpixel Segmentation Iterative Algorithm Based on Ball-k-means Clustering

针对超像素分割问题,为了进一步提高超像素边缘贴合度,提出了一种基于球簇聚类的超像素分割迭代算法。首先,将超像素视为五维超球体,对图像进行均匀分割得到初始超像素及其中心和半径;其次,依据邻接超像素中心间的距离及其半径搜索近邻超像素;然后,利用超像素与其近邻超像素中心间的距离,将超像素划分为稳定区和多个环形活跃区;最后,每个环形活跃区内的像素点仅根据其与部分近邻超像素中心的距离将其分入最近的超像素,如此迭代实现超像素分割。为了减少距离计算量以加快收敛速度,给出了近邻超像素关系判定定理,对像素点的超像素类标签设计了一种自适应分区更新策略。在BSD500数据集上与多种典型超像素分割算法进行了实验对比,结果表明该算法对不同类型图像的分割效果均较好,边缘贴合度更高,且受参数影响较小,分割结果更稳定。

Considering the problem of superpixel segmentation,this paper propose an iterative algorithm of superpixel segmentation based on Ball-k-means clustering to further improve the edge fit of superpixels.Firstly,the superpixels are regarded as five-dimensional hyperspheres,and the image is evenly segmented to obtain the initial superpixels.Secondly,the neighbor superpixels are searched according to the radius and distance between the centers of adjacent superpixels.Then,using the distances between the superpixels and their neighbor superpixel centers,the superpixels are divided into a stable region and multiple ring active regions.Finally,the pixels in each annular active area are divided into the nearest neighbor superpixel only according to their distance from the center of some neighbor superpixels,so as to realize the superpixel segmentation iteratively.In order to reduce the distance calculation and speed up the convergence,a judgment theorem of the relation between the nearest neighbor superpixels is given,and an adaptive partition updating strategy is designed for the superpixel class labels of pixels.Experimental comparison and analysis on BSD500 data set show that the proposed algorithm has better segmentation effect on different types of images,with higher edge fitting degree,less influence by parameters,and more stable segmentation results.