Enhanced-Alignment Measure For Binary Foreground Map Evaluation
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.
Publication Date
1-1-2018
Publication Title
IJCAI International Joint Conference on Artificial Intelligence
Volume
2018-July
Number of Pages
698-704
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.24963/ijcai.2018/97
Copyright Status
Unknown
Socpus ID
85055452797 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/85055452797
STARS Citation
Fan, Deng Ping; Gong, Cheng; Cao, Yang; Ren, Bo; and Cheng, Ming Ming, "Enhanced-Alignment Measure For Binary Foreground Map Evaluation" (2018). Scopus Export 2015-2019. 10533.
https://stars.library.ucf.edu/scopus2015/10533