Enhanced-alignment Measure for Binary Foreground Map Evaluation

TLDR

Current binary foreground map evaluation metrics assess pixel‑wise or structural errors separately, yet human vision relies on both global and local information. This study introduces the Enhanced‑measure (E‑measure) to improve binary foreground map evaluation. E‑measure integrates local pixel values with the image‑level mean to jointly capture statistics and matching, and outperforms existing metrics on four datasets across five meta‑measures. E‑measure achieves substantial gains, improving application ranking by 9.08%–19.65% and outperforming other metrics on most meta‑measures.

Abstract

The existing binary foreground map (FM) measures 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 improvement ranging from 9.08% to 19.65% compared with other popular measures.