基于快速SLIC的图像超像素算法

针对SLIC(Simple Linear Iterative Clustering)算法在超像素聚类过程中耗时较长的缺陷,提出一种基于快速SLIC的图像超像素算法。该算法首先剔除在颜色空间上与聚类中心相似度较低的像素,从而仅用部分近邻像素更新聚类中心,以确保聚类中心快速达到稳定并阻止误差传播,提高边缘命中率;其次,在初始化网格后,将每个超像素的边缘像素视为不稳定像素,将超像素的非边缘像素视为稳定像素并保持稳定像素的类别不变;最后,通过对不稳定像素进行迭代标记来实现快速超像素图像分割。在MATLAB环境下分别对所提算法与6种对比算法进行测试,在超像素个数相同的情况下,所提算法在BSD500数据集上与经典的SLIC算法相比分割误差率降低5%,分割精度提高0.5%,运行时间减少0.18 s。实验结果表明,与主流的超像素算法相比,所提算法在提升超像素分割质量的同时能够有效降低算法的计算复杂度。

Fast Simple Linear Iterative Clustering for Image Superpixel Algorithm

Simple linear iterative clustering(SLIC)takes a long time in the process of superpixel clustering.To address this drawback,this paper proposed a fast SLIC algorithm for image superpixel.Firstly,the algorithm removes the pixels that are clearly different from the clustering center in a superpixel area,and then uses the remaining pixels to update the clustering center.The operation ensures that the clustering center achieves convergence quickly,and prevents error propagation.Secondly,the edge pi-xels of each superpixel area are considered as active pixels while the non-edge pixels are considered as stable pixels that belong to one fixed class by initializing grids on the original image.Finally,fast superpixel image segmentation is achieved by labeling unstable pixels iteratively.This paper performed six comparative algorithms and the proposed algorithm on the Benchmark BSD500 under the environment of MATLAB.Compared with SLIC algorithm,the segmentation error rate of the proposed algorithm is reduced by 5%,the segmentation accuracy is improved by 0.5%,and the running time is 0.18 s less than the later.The experimental results show that the proposed algorithm can improve the quality of superpixel segmentation while effectively reducing the computational complexity of the algorithm compared to popular superpixel algorithms.