IEEE Trans Image Process. 2017 May;26(5):2246-2260. doi: 10.1109/TIP.2017.2651395. Epub 2017 Jan 11.
In this paper, we propose a new framework for hierarchical image segmentation based on iterative contraction and merging. In the proposed framework, we treat the hierarchical image segmentation problem as a sequel of optimization problems, with each optimization process being realized by a contraction-and-merging process to identify and merge the most similar data pairs at the current resolution. At the beginning, we perform pixel-based contraction and merging to quickly combine image pixels into initial region-elements with visually indistinguishable intra-region color difference. After that, we iteratively perform region-based contraction and merging to group adjacent regions into larger ones to progressively form a segmentation dendrogram for hierarchical segmentation. Comparing with the state-of-the-art techniques, the proposed algorithm can not only produce high-quality segmentation results in a more efficient way, but also keep a lot of boundary details in the segmentation results.
在本文中，我们提出了一种基于迭代收缩和合并的新的层次图像分割框架。在提出的框架中，我们将层次图像分割问题视为一系列优化问题，每个优化过程都是通过收缩和合并过程来识别和合并当前分辨率下最相似的数据对来实现的。在开始时，我们执行基于像素的收缩和合并，以快速将图像像素合并为初始区域元素，这些元素在视觉上具有不可区分的内部区域颜色差异。之后，我们迭代地执行基于区域的收缩和合并，将相邻区域组合成更大的区域，以逐步形成层次分割的分割树。与最先进的技术相比，该算法不仅可以更有效地生成高质量的分割结果，而且还可以在分割结果中保留许多边界细节。
