CAJ | 학술논문

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。
제출일충기우개진희소자공간취류적도상분할방법。수선장도상진행과분할득도일사균균구역칭위초상소，병제취초상소적안색직방도작위기특정；연후건립특정수거적개진희소자공간표시병유차구조도상사도구진，최후이용보취류산법득도초상소적취류결과병작위도상분할결과。실험결과표명，본문제출적개진희소자공간취류방법구유량호적취류성능，대조성구유일정적로봉성；용우자연도상능구득도경호적분할효과。
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.