{"type":"ScholarlyArticle","title":"Interactive Image Segmentation via Pairwise Likelihood Learning","authors":[{"name":"Tao Wang","slug":"tao-wang","given":"Tao","family":"Wang","affiliation":""},{"name":"Quansen Sun","slug":"quansen-sun","given":"Quansen","family":"Sun","affiliation":""},{"name":"Qi Ge","slug":"qi-ge","given":"Qi","family":"Ge","affiliation":""},{"name":"Zexuan Ji","slug":"zexuan-ji","given":"Zexuan","family":"Ji","affiliation":""},{"name":"Qiang Chen","slug":"qiang-chen","given":"Qiang","family":"Chen","affiliation":""},{"name":"Guiyu Xia","slug":"guiyu-xia","given":"Guiyu","family":"Xia","affiliation":""}],"abstract":"This paper presents an interactive image segmentation approach where the segmentation problem is formulated as a probabilistic estimation manner. Instead of measuring the distances between unseeded pixels and seeded pixels, we measure the similarities between pixel pairs and seed pairs to improve the robustness to the seeds. The unary prior probability of each pixel belonging to the foreground F and background B can be effectively estimated based on the similarities with label pairs (F, F),(F, B),(B, F) and (B, B). Then a likelihood learning framework is proposed to fuse the region and boundary information of the image by imposing the smoothing constraint on the unary potentials. Experiments on challenging data sets demonstrate that the proposed method can obtain better performance than state-of-the-art methods.","doi":"10.24963/ijcai.2017/412","url":"https://ijcai.org/proceedings/2017/412","pdf":"https://ijcai.org/proceedings/2017/0412.pdf","markdown":"https://ijcai.org/proceedings/2017/412.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/412","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning S-Z","track":"Main track","number":412,"pages":{"first":2957,"last":2963},"keywords":["Uncertainty in AI: Exact Probabilistic Inference","Machine Learning: Classification","Machine Learning: Semi-Supervised Learning","Machine Learning: Feature Selection/Construction"],"videos":[],"references":[{"position":1,"raw":"[Boykov and Jolly, 2001] Yuri Y. 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