{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T18:34:00Z","timestamp":1776278040444,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.24963\/ijcai.2018\/97","type":"proceedings-article","created":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T05:49:10Z","timestamp":1530769750000},"page":"698-704","source":"Crossref","is-referenced-by-count":1210,"title":["Enhanced-alignment Measure for Binary Foreground Map Evaluation"],"prefix":"10.24963","author":[{"given":"Deng-Ping","family":"Fan","sequence":"first","affiliation":[{"name":"College of Computer and Control Engineering, Nankai University"}]},{"given":"Cheng","family":"Gong","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Nankai University"}]},{"given":"Yang","family":"Cao","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Nankai University"}]},{"given":"Bo","family":"Ren","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Nankai University"}]},{"given":"Ming-Ming","family":"Cheng","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Nankai University"}]},{"given":"Ali","family":"Borji","sequence":"additional","affiliation":[{"name":"Center for Research in Computer Vision, Central Florida University"}]}],"member":"10584","event":{"name":"Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}","theme":"Artificial Intelligence","location":"Stockholm, Sweden","acronym":"IJCAI-2018","number":"27","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2018,7,13]]},"end":{"date-parts":[[2018,7,19]]}},"container-title":["Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T05:49:49Z","timestamp":1530769789000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2018\/97"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2018\/97","relation":{},"subject":[],"published":{"date-parts":[[2018,7]]}}}