A hierarchical image segmentation is a set of image segmentations at different detail levels. However, objects (or even parts of the same object) may appear at different scales due to their size differences or to their distinct distances from the camera. One possible solution to cope with that is to realign the hierarchy such that every region containing an object (or its parts) is at the same level. In this work, we have explored the use of regression models to predict score values for regions belonging to a hierarchy of partitions, which are used to realign it. We have also proposed a new score calculation and a new assessment strategy considering all user-defined segmentations that exist in the ground-truth. Experimental results have pointed out that the use of new proposed score was able to improve final segmentation results.

分层图像分割是一组在不同细节级别的图像分割。但是，由于物体的大小差异或与相机的距离不同，物体（甚至同一物体的一部分）可能会以不同的比例出现。一种可能的解决方案是重新调整层次结构，以使包含对象（或其部分）的每个区域都处于同一级别。在这项工作中，我们探索了使用回归模型来预测属于分区层次结构的区域的分数值，这些分数用于对其进行重新对齐。考虑到地面真相中存在的所有用户定义的细分，我们还提出了新的分数计算和新的评估策略。实验结果指出，使用新提议的分数能够改善最终的细分结果。