{"type":"ScholarlyArticle","title":"Deep Propagation Based Image Matting","authors":[{"name":"Yu Wang","slug":"yu-wang","given":"Yu","family":"Wang","affiliation":"School of Information Science and Engineering , Shandong Normal University , China"},{"name":"Yi Niu","slug":"yi-niu","given":"Yi","family":"Niu","affiliation":"School of Information Science and Engineering , Shandong Normal University , China"},{"name":"Peiyong Duan","slug":"peiyong-duan","given":"Peiyong","family":"Duan","affiliation":"School of Information Science and Engineering , Shandong Normal University , China"},{"name":"Jianwei Lin","slug":"jianwei-lin","given":"Jianwei","family":"Lin","affiliation":"School of Information Science and Engineering , Shandong Normal University , China"},{"name":"Yuanjie Zheng","slug":"yuanjie-zheng","given":"Yuanjie","family":"Zheng","affiliation":"School of Information Science and Engineering , Shandong Normal University , China; Key Lab of Intelligent Computing and Information Security in Universities of Shandong , China; Shandong Provincial Key Lab for Distributed Computer Software Novel Technology , China; Institute of Biomedical Sciences , Shandong Normal University , China"}],"abstract":"In this paper, we propose a deep propagation based image matting framework by introducing deep learning into learning an alpha matte propagation principal. Our deep learning architecture is a concatenation of a deep feature extraction module, an affinity learning module and a matte propagation module. These three modules are all differentiable and can be optimized jointly via an end-to-end training process. Our framework results in a semantic-level pairwise similarity of pixels for propagation by learning deep image representations adapted to matte propagation. It combines the power of deep learning and matte propagation and can therefore surpass prior state-of-the-art matting techniques in terms of both accuracy and training complexity, as validated by our experimental results from 243K images created based on two benchmark matting databases.","doi":"10.24963/ijcai.2018/139","url":"https://ijcai.org/proceedings/2018/139","pdf":"https://ijcai.org/proceedings/2018/0139.pdf","markdown":"https://ijcai.org/proceedings/2018/139.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/139","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Computer Vision","track":"Main track","number":139,"pages":{"first":999,"last":1006},"keywords":["Machine Learning: Deep Learning","Computer Vision: Computer Vision","Computer Vision: Computational Photography, Photometry, Shape from X"],"videos":[],"references":[{"position":1,"raw":"[Aksoy et al., 2017] Yagız Aksoy, Tunc ¸Ozan Aydın, and Marc Pollefeys. 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