图像分割的改进稀疏子空间聚类方法

Improved sparse subspace clustering method for image segmentation

提出一种基于改进稀疏子空间聚类的图像分割方法。首先将图像进行过分割得到一些均匀区域称为超像素,并提取超像素的颜色直方图作为其特征;然后建立特征数据的改进稀疏子空间表示并由此构造图相似度矩阵,最后利用谱聚类算法得到超像素的聚类结果并作为图像分割结果。实验结果表明,本文提出的改进稀疏子空间聚类方法具有良好的聚类性能,对噪声具有一定的鲁棒性;用于自然图像能够得到更好的分割效果。

A novel image segmentation method based on improved sparse subspace clustering is presented. The image to be segmented is over-partitioned into some uniform sub-regions called superpixels,and color histo-gram of each superpixel is computed as its feature data.Then by employing an improved sparse subspace repre-sentation model,the sparse representation coefficient matrix is computed and used to construct the affinity ma-trix of a graph.Finally,the spectral clustering algorithm is used to obtain the image segmentation result.Ex-periments show that the proposed improved sparse subspace clustering method performs well in clustering and is robust to noise.It can obtain good segmentation results for natural color images.