Ensemble Segmentation Using Efficient Integer Linear Programming
Ensemble Segmentation Using Efficient Integer Linear Programming
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DOI：
10.1109/tpami.2011.280
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发表时间：
2012-10
影响因子：
23.6
通讯作者：
Amir Alush;J. Goldberger
中科院分区：
文献类型：
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作者：
Amir Alush;J. Goldberger
我们提出了一种将图像的多个分割组合成单个分割的方法，在某种意义上是平均分割，以获得更可靠和准确的分割结果。目标是在“分割空间”中找到一个接近所有单独分割的点。我们提出了一种分段平均算法。图像首先被过度分割为超像素。接下来，将每个分割投影到超像素图上。将 EM 算法与整数线性规划相结合的实例应用于相邻超像素的二进制合并决策集，以获得平均分割。除了分段平均之外，该算法还报告每个分段的可靠性。该算法的性能在伯克利分割数据集的手动注释图像和自动分割算法的结果上得到了证明。
We present a method for combining several segmentations of an image into a single one that in some sense is the average segmentation in order to achieve a more reliable and accurate segmentation result. The goal is to find a point in the “space of segmentations” which is close to all the individual segmentations. We present an algorithm for segmentation averaging. The image is first oversegmented into superpixels. Next, each segmentation is projected onto the superpixel map. An instance of the EM algorithm combined with integer linear programming is applied on the set of binary merging decisions of neighboring superpixels to obtain the average segmentation. Apart from segmentation averaging, the algorithm also reports the reliability of each segmentation. The performance of the proposed algorithm is demonstrated on manually annotated images from the Berkeley segmentation data set and on the results of automatic segmentation algorithms.