Enhanced-alignment Measure for Binary Foreground Map Evaluation

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.

Authors

• Deng-Ping Fan
• Cheng Gong
• Yang Cao University of Science and Technology of China
• Bo Ren Tencent Youtu Lab, China
• Ming-Ming Cheng Nankai University NKIARI
• Ali Borji

Keywords

• Computer Vision: 2D and 3D Computer Vision
• Computer Vision: Computer Vision