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.

Keywords

PixelMeasure (data warehouse)Computer scienceArtificial intelligenceGround truthRanking (information retrieval)Binary numberMatching (statistics)Pattern recognition (psychology)RangingNoise (video)Computer visionBinary dataImage (mathematics)Data miningMathematicsStatistics

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Year
2018
Type
preprint
Pages
698-704
Citations
1378
Access
Closed

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Deng-Ping Fan, Cheng Gong, Yang Cao et al. (2018). Enhanced-alignment Measure for Binary Foreground Map Evaluation. , 698-704. https://doi.org/10.24963/ijcai.2018/97

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DOI
10.24963/ijcai.2018/97