基于测地线的超像素谱聚类彩色图像分割

Color image segmentation of spectral clustering based on super pixel geodesic

在图像分割中谱聚类算法得到了广泛的应用,但传统谱聚类算法易受到彩色图像大小和相似性测度的影响,导致计算量大和分割精度低的问题。为了解决这两个问题,提出一种新的基于超像素集测地线特征的谱聚类分割算法。该方法通过对彩色图像进行预分割得到超像素集,并以超像素集为基础构造加权图,利用测地线距离特征和颜色特征构造权值矩阵,最后应用NJW(Ng-Jordan-Weiss)算法得到最终的分割结果。对比实验结果表明该算法在分割精度和计算复杂度上都有较大改善。

Spectral clustering algorithm has been widely used in image segmentation, however, traditional spectral cluster- ing algorithms are susceptible to the effect of color image size and similarity measure. They create large amount of calcu- lation and the poor segmentation result. In order to solve these two problems, this paper proposes a new spectral clustering segmentation algorithm based on geodesic characteristics of super pixel set. It uses the pre-segmentation to get the super set of pixels. Then it constructs a weighted graph, and uses the geodesic distance and color features to build a weight matrix. It uses NJW （Ng-Jordan-Weiss） algorithm to get the segmentation result. The experimental results show that the segmenta- tion accuracy and computational complexity of the algorithm in this paper are improved substantially.