{"type":"ScholarlyArticle","title":"MEnet: A Metric Expression Network for Salient Object Segmentation","authors":[{"name":"Shulian Cai","slug":"shulian-cai","given":"Shulian","family":"Cai","affiliation":"Fujian Key Laboratory of Sensing and Computing for Smart City , Xiamen University , China"},{"name":"Jiabin Huang","slug":"jiabin-huang","given":"Jiabin","family":"Huang","affiliation":"Fujian Key Laboratory of Sensing and Computing for Smart City , Xiamen University , China"},{"name":"Delu Zeng","slug":"delu-zeng","given":"Delu","family":"Zeng","affiliation":"School of Mathematics , South China University of Technology , China"},{"name":"Xinghao Ding","slug":"xinghao-ding","given":"Xinghao","family":"Ding","affiliation":"Fujian Key Laboratory of Sensing and Computing for Smart City , Xiamen University , China"},{"name":"John Paisley","slug":"john-paisley","given":"John","family":"Paisley","affiliation":"Department of Electrical Engineering , Columbia University , USA"}],"abstract":"Recent CNN-based saliency models have achieved excellent performance on public datasets, but most are sensitive to distortions from noise or compression. In this paper, we propose an end-to-end generic salient object segmentation model called\r\nMetric Expression Network (MEnet) to overcome this drawback. We construct a topological metric space where the implicit metric is determined by a deep network. In this latent space, we can group pixels within an observed image semantically into\r\ntwo regions, based on whether they are in a salient region or a non-salient region in the image. We carry out all feature extractions at the pixel level, which makes the output boundaries of the salient object finely-grained. Experimental results show\r\nthat the proposed metric can generate robust salient maps that allow for object segmentation. By testing the method on several public benchmarks, we show that the performance of MEnet achieves excellent results. We also demonstrate that the proposed\r\nmethod outperforms previous CNN-based methods on distorted images.","doi":"10.24963/ijcai.2018/83","url":"https://ijcai.org/proceedings/2018/83","pdf":"https://ijcai.org/proceedings/2018/0083.pdf","markdown":"https://ijcai.org/proceedings/2018/83.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/83","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":83,"pages":{"first":598,"last":605},"keywords":["Machine Learning: Deep Learning","Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation","Computer Vision: Computer Vision"],"videos":[],"references":[{"position":1,"raw":"[Badrinarayanan et al., 2017] Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla. 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