论文标题
最通用的方式是对齐真实和预测的段
The most general manner to injectively align true and predicted segments
论文作者
论文摘要
Kirilov等人（2019年）开发了一种称为Panoptic质量（PQ）的度量，以评估图像分割方法。该指标基于混乱表，并将预测的一个预测与地面真理分段进行了比较。在此比较中，唯一的非直接部分是对齐两个分割中的片段。只有当该对齐是部分射击时，指标才能很好地工作。 Kirilov等人（2019）列表3个对齐方式的理想属性：它应该简单，可解释且有效地计算。有许多定义可以保证部分射击和这3个属性。我们提出了最弱的：一个既足够且必要的，以确保对齐是部分生物。这种新条件有效地计算和自然。它简单地说，正确预测的元素的数量（在图像分割，像素中）应大于错过的数量，并且比虚假元素的数量大。这比Kirilov等人（2019）中的提议严格弱。在公式中，而不是| tp |> | fn \ textbar | + | fp |，弱条件要求| tp |> | fn |和| tp | > | fp |。我们从理论和经验上评估了新的对齐条件。
Kirilov et al (2019) develop a metric, called Panoptic Quality (PQ), to evaluate image segmentation methods. The metric is based on a confusion table, and compares a predicted to a ground truth segmentation. The only non straightforward part in this comparison is to align the segments in the two segmentations. A metric only works well if that alignment is a partial bijection. Kirilov et al (2019) list 3 desirable properties for a definition of alignment: it should be simple, interpretable and effectively computable. There are many definitions guaranteeing a partial bijection and these 3 properties. We present the weakest: one that is both sufficient and necessary to guarantee that the alignment is a partial bijection. This new condition is effectively computable and natural. It simply says that the number of correctly predicted elements (in image segmentation, the pixels) should be larger than the number of missed, and larger than the number of spurious elements. This is strictly weaker than the proposal in Kirilov et al (2019). In formulas, instead of |TP|> |FN\textbar| + |FP|, the weaker condition requires that |TP|> |FN| and |TP| > |FP|. We evaluate the new alignment condition theoretically and empirically.
