Image Segmentation Using Hierarchical Merge Tree.
Image Segmentation Using Hierarchical Merge Tree.
复制标题
使用分层合并树进行图像分割。
DOI：
10.1109/tip.2016.2592704
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发表时间：
2016-10
期刊：
影响因子：
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通讯作者：
Tasdizen T
中科院分区：
文献类型：
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作者：
Ting Liu;Seyedhosseini M;Tasdizen T
本文研究了计算机视觉中最基本的问题之一：图像分割。我们提出了一个监督的分层方法对象无关的图像分割。从过分割超像素开始，我们使用树结构来表示区域合并的层次结构，通过该结构，我们将分割图像区域的问题简化为找到一组对树节点的标签分配。我们制定的树结构作为一个受约束的条件模型相关联的区域合并使用集成边界分类器预测的可能性。然后可以通过有效地找到模型的全局最优解来推断最终分割。我们还提出了一个迭代的训练和测试算法，生成各种树结构，并将它们结合起来，通过分割积累来强调准确的边界。在六个公共数据集上的实验结果和与其他方法的比较表明，我们的方法达到了最先进的区域精度，在没有语义先验的图像分割中具有竞争力。
This paper investigates one of the most fundamental computer vision problems: image segmentation. We propose a supervised hierarchical approach to object-independent image segmentation. Starting with over-segmenting superpixels, we use a tree structure to represent the hierarchy of region merging, by which we reduce the problem of segmenting image regions to finding a set of label assignment to tree nodes. We formulate the tree structure as a constrained conditional model to associate region merging with likelihoods predicted using an ensemble boundary classifier. Final segmentations can then be inferred by finding globally optimal solutions to the model efficiently. We also present an iterative training and testing algorithm that generates various tree structures and combines them to emphasize accurate boundaries by segmentation accumulation. Experiment results and comparisons with other recent methods on six public data sets demonstrate that our approach achieves state-of-the-art region accuracy and is competitive in image segmentation without semantic priors.