The existing binary foreground map (FM) measures to address various types of
errors in either pixel-wise or structural ways. These measures consider
pixel-level match or image-level information independently, while cognitive
vision studies have shown that human vision is highly sensitive to both global
information and local details in scenes. In this paper, we take a detailed look
at current binary FM evaluation measures and propose a novel and effective
E-measure (Enhanced-alignment measure). Our measure combines local pixel values
with the image-level mean value in one term, jointly capturing image-level
statistics and local pixel matching information. We demonstrate the superiority
of our measure over the available measures on 4 popular datasets via 5
meta-measures, including ranking models for applications, demoting generic,
random Gaussian noise maps, ground-truth switch, as well as human judgments. We
find large improvements in almost all the meta-measures. For instance, in terms
of application ranking, we observe improvementrangingfrom9.08% to 19.65%
compared with other popular measures.

本研究提出了一种新的增强对准度量方法 (E-measure)，结合了局部像素值和图像级平均值，并与现有的二元前景图测量方法相比，在四个广泛应用于计算机视觉领域的数据集上表现出较高的优越性。