{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/structure-measure-a-new-way-to-evaluate","title":"Structure-measure: A New Way to Evaluate Foreground Maps","arxiv_id":"1708.00786","date":"2017-08-02","proceeding":"ICCV 2017 10","authors":["Deng-Ping Fan","Ming-Ming Cheng","Yun Liu","Tao Li","Ali Borji"],"abstract":"Foreground map evaluation is crucial for gauging the progress of object\nsegmentation algorithms, in particular in the filed of salient object detection\nwhere the purpose is to accurately detect and segment the most salient object\nin a scene. Several widely-used measures such as Area Under the Curve (AUC),\nAverage Precision (AP) and the recently proposed Fbw have been utilized to\nevaluate the similarity between a non-binary saliency map (SM) and a\nground-truth (GT) map. These measures are based on pixel-wise errors and often\nignore the structural similarities. Behavioral vision studies, however, have\nshown that the human visual system is highly sensitive to structures in scenes.\nHere, we propose a novel, efficient, and easy to calculate measure known an\nstructural similarity measure (Structure-measure) to evaluate non-binary\nforeground maps. Our new measure simultaneously evaluates region-aware and\nobject-aware structural similarity between a SM and a GT map. We demonstrate\nsuperiority of our measure over existing ones using 5 meta-measures on 5\nbenchmark datasets.","url_abs":"http://arxiv.org/abs/1708.00786v1","url_pdf":"http://arxiv.org/pdf/1708.00786v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"structure-measure-a-new-way-to-evaluate","repo_url":"https://github.com/DengPingFan/S-measure","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"salient-object-detection","task_name":"RGB Salient Object Detection"},{"task_slug":"salient-object-detection-1","task_name":"Salient Object Detection"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"small-object-detection","task_name":"Small Object Detection"},{"task_slug":"video-object-detection","task_name":"Video Object Detection"},{"task_slug":"video-salient-object-detection","task_name":"Video Salient Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1708.00786","atlas_url":"https://app.syntology.ai/?focus=1708.00786","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}