Image Segmentation by Probabilistic Bottom-Up Aggregation and Cue Integration
Image Segmentation by Probabilistic Bottom-Up Aggregation and Cue Integration
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DOI：
10.1109/tpami.2011.130
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发表时间：
2012-02-01
影响因子：
23.6
通讯作者：
Basri, Ronen
中科院分区：
文献类型：
--
作者：
Alpert, Sharon;Galun, Meirav;Basri, Ronen
我们提出了一个自底向上的聚合方法来图像分割。从图像开始，我们执行一系列步骤，其中像素逐渐合并以产生越来越大的区域。在每一步中，我们考虑相邻区域的配对，并提供一个概率度量来评估它们是否应该包含在同一段中。我们的概率公式考虑到强度和纹理分布在每个区域周围的局部区域。它进一步结合先验的基础上的几何形状的区域。最后，后验的强度和纹理线索的基础上结合使用“专家的混合物”配方。这种概率方法被集成到一个图形粗化计划，提供了一个完整的分层分割的图像。该算法的复杂度与图像像素数成线性关系，几乎不需要用户调整参数。此外，我们提供了一种新的图像分割算法的评估方案，试图避免人类的语义考虑范围之外的分割算法。使用这种新的评估方案，我们测试我们的方法，并提供了一个比较现有的分割算法。
We present a bottom-up aggregation approach to image segmentation. Beginning with an image, we execute a sequence of steps in which pixels are gradually merged to produce larger and larger regions. In each step, we consider pairs of adjacent regions and provide a probability measure to assess whether or not they should be included in the same segment. Our probabilistic formulation takes into account intensity and texture distributions in a local area around each region. It further incorporates priors based on the geometry of the regions. Finally, posteriors based on intensity and texture cues are combined using "a mixture of experts" formulation. This probabilistic approach is integrated into a graph coarsening scheme, providing a complete hierarchical segmentation of the image. The algorithm complexity is linear in the number of the image pixels and it requires almost no user-tuned parameters. In addition, we provide a novel evaluation scheme for image segmentation algorithms, attempting to avoid human semantic considerations that are out of scope for segmentation algorithms. Using this novel evaluation scheme, we test our method and provide a comparison to several existing segmentation algorithms.