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Enhanced-alignment Measure for Binary Foreground Map Evaluation

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arxiv 1805.10421 v2 pith:DFWVDGWP submitted 2018-05-26 cs.CV

Deng-Ping Fan , Cheng Gong , Yang Cao , Bo Ren , Ming-Ming Cheng , Ali Borji This is my paper · ORCID
classification cs.CV
keywords measuresmeasurebinaryimage-levelinformationlocalenhanced-alignmentevaluation
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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 as human judgments. We find large improvements in almost all the meta-measures. For instance, in terms of application ranking, we observe improvementrangingfrom9.08% to 19.65% compared with other popular measures.

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