Enhanced-alignment Measure for Binary Foreground Map Evaluation
Deng-Ping Fan, Cheng Gong, Yang Cao, Bo Ren, Ming-Ming Cheng, Ali, Borji

TL;DR
This paper introduces the E-measure, a novel binary foreground map evaluation metric that combines local pixel details with global image statistics, showing significant improvements over existing measures across multiple datasets.
Contribution
The paper proposes the E-measure, integrating local and global information for more accurate binary foreground map evaluation, outperforming existing measures on various datasets.
Findings
E-measure outperforms existing measures on 4 datasets
Significant improvements in application ranking accuracy
Enhanced correlation with human judgment
Abstract
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…
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Taxonomy
TopicsVisual Attention and Saliency Detection · Advanced Image and Video Retrieval Techniques · Video Surveillance and Tracking Methods
