Enhanced-alignment Measure for Binary Foreground Map Evaluation Deng-Ping Fan1, Cheng Gong1, Yang Cao1, Bo Ren1, Ming-Ming Cheng1, Ali Borji2 1 Nankai University 2 Central Florida University Poster #2351 1. Problem Image GT Map1 Noise IoU, CM[1], 𝑭 𝜷 𝒘 [2], VQ[3], S-measure[4] prefer the noise map. None of them consider both local and global simultaneously. [1] Movahedi et al. Design and perceptual validation of performance measures for salient object segmentation. CVPRW, 2010. [2] Margolin et al. How to evaluate foreground maps? CVPR, 2014. [3] Shi et al. Visual quality evaluation of image object segmentation: Subjective assessment and objective measure. TIP, 2015. [4] Fan et al. Structure-measure: A New Way to Evaluate Foreground Maps. ICCV, 2017. 3. Solutions Bias matrix Alignment matrix Enhanced function 𝜑 𝐼 =𝐼− 𝜇 𝐼 ∙𝐴, 𝜉 𝐹𝑀 = 2 𝜑 𝐺𝑇 ∘ 𝜑 𝐹𝑀 𝜑 𝐺𝑇 ∘ 𝜑 𝐺𝑇 + 𝜑 𝐹𝑀 ∘ 𝜑 𝐹𝑀 , ∅ 𝐹𝑀 =𝑓 𝜉 𝐹𝑀 . 4. Experiments MM1. Application Ranking MM4. Human Ranking GT Rank1 Rank2 Rank3 MM2. SOTA vs. Generic 2. Motivation 1. Global information can be captured by the eye movement. Image GT Map1 Generic MM3. SOTA vs. Noise 2. Local details recorded by focusing the special image region. Image GT Map1 Noise Examples 5. Results 9.08% to 19.65% improvement vs. popular measures. Visit our Group! http://mmcheng.net/ http://dpfan.net/e-measure/