彩色图像分割中基于图上半监督学习算法研究

Color Image Segmentation Using Graph-Based Semi-Supervised Learning

提出一种新的基于图上半监督学习的彩色图像前景/背景分割模型与算法.该算法的目的是利用人工标定的部分像素点分割信息以实现对整幅图像的分割.通过结合像素点颜色特征和像素点颜色与前景/背景颜色的相似性特征,构造了新的图节点之间的双高斯权重函数,并对此提出自适应的参数选择策略与彩色图像半监督分割的能量模型,通过优化该能量模型将已知像素点的标号信息扩散到未知像素点.实验结果表明,所提出的新算法较已有算法具有更高的分割精度,因此具有重要的应用价值.

A novel color image foreground/background segmentation model by semi-supervised learning is proposed. The essence is how to use the labeled pixels to achieve the whole image seg- mentation. Combining the color similarity between neighboring pixels and the color similarity between the unknown pixel and the known foreground/backgr0und pixels, a double-Gaussian func- tion for the weight of graph nodes is constructed. And an adaptive parameter selection strategy and an energy model of semi-supervised segmentation are presented. The energy model is used to predict the labels of the unlabeled points by an optimization process. The experiments demon- strate the better segmentation accuracy than the competing algorithms.