Generative Image Segmentation Using Random Walks with Restart
Generative Image Segmentation Using Random Walks with Restart
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DOI：
10.1007/978-3-540-88690-7_20
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发表时间：
2008-10
期刊：
影响因子：
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通讯作者：
Tae Hoon Kim;Kyoung Mu Lee;Sang Uk Lee
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文献类型：
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作者：
Tae Hoon Kim;Kyoung Mu Lee;Sang Uk Lee
我们考虑的问题，多标签，监督图像分割时，一些像素的初始标签。在本文中，我们提出了一个新的生成式图像分割算法的可靠的多标签分割在自然图像。相对于大多数现有的算法，专注于标签间的歧视，我们解决的问题，找到每个标签的生成模型。我们的算法的主要优点是，它产生了两个困难的问题，即弱边界问题和纹理问题的分割效果非常好。此外，单标签图像分割是可能的。这些都是通过使用重启随机游走（RWR）设计生成模型来实现的。合成和自然图像的实验结果表明，我们的算法的相关性和准确性。
We consider the problem of multi-label, supervised image segmentation when an initial labeling of some pixels is given. In this paper, we propose a new generative image segmentation algorithm for reliable multi-label segmentations in natural images. In contrast to most existing algorithms which focus on the inter-label discrimination, we address the problem of finding the generative model for each label. The primary advantage of our algorithm is that it produces very good segmentation results under two difficult problems: theweak boundary problemand thetexture problem. Moreover, single-label image segmentation is possible. These are achieved by designing the generative model with the Random Walks with Restart (RWR). Experimental results with synthetic and natural images demonstrate the relevance and accuracy of our algorithm.