Augmented tree partitioning for interactive image segmentation
Augmented tree partitioning for interactive image segmentation
复制标题
DOI：
10.1109/icip.2008.4712249
复制
发表时间：
2008-12
期刊：
影响因子：
--
通讯作者：
Yangqing Jia;Jingdong Wang;Changshui Zhang;Xiansheng Hua
中科院分区：
文献类型：
--
作者：
Yangqing Jia;Jingdong Wang;Changshui Zhang;Xiansheng Hua
本文提出了一种基于增广树分割的半监督图像快速分割方法。与许多使用图结构对图像建模的现有方法不同，我们使用了一种称为增强树的基于树的结构，该结构通过将几个抽象标签节点扩展到原始图的最小生成树来构建。然后，我们将图像分割建模为增广树上的分区问题。采用动态规划方法有效地解决了优化问题。实验结果表明，该方法具有较好的分割效果，分割速度比基于图的方法快得多。
In this paper, we propose a new fast semi-supervised image segmentation method based on augmented tree partitioning. Unlike many existing methods that use a graph structure to model the image, we use a tree-based structure called the augmented tree, which is built up by augmenting several abstract label nodes to the minimum spanning tree of the original graph. We then model image segmentation as the partitioning problem on the augmented tree. Dynamic programming is used to efficiently solve the optimization problem. Experimental results show that our method gives competitive segmentation results, and the speed is much faster than graph- based methods.