{"type":"ScholarlyArticle","title":"Deep Joint Semantic-Embedding Hashing","authors":[{"name":"Ning Li","slug":"ning-li","given":"Ning","family":"Li","affiliation":"School of Electronic Engineering , Xidian University , Xi'an 710071 , China"},{"name":"Chao Li","slug":"chao-li","given":"Chao","family":"Li","affiliation":"School of Electronic Engineering , Xidian University , Xi'an 710071 , China"},{"name":"Cheng Deng","slug":"cheng-deng","given":"Cheng","family":"Deng","affiliation":"School of Electronic Engineering , Xidian University , Xi'an 710071 , China"},{"name":"Xianglong Liu","slug":"xianglong-liu","given":"Xianglong","family":"Liu","affiliation":"Beihang University , Beijing 100191 , China"},{"name":"Xinbo Gao","slug":"xinbo-gao","given":"Xinbo","family":"Gao","affiliation":"School of Electronic Engineering , Xidian University , Xi'an 710071 , China"}],"abstract":"Hashing has been widely deployed to large-scale image retrieval due to its low storage cost and fast query speed. Almost all deep hashing methods do not sufficiently discover semantic correlation from label information, which results in the learned hash codes less discriminative. In this paper, we propose a novel Deep Joint Semantic-Embedding Hashing (DSEH) approach that contains LabNet and ImgNet. Specifically, LabNet is explored to capture abundant semantic correlation between sample pairs and supervise ImgNet from semantic level and hash codes level, which is conductive to the generated hash codes being more discriminative and similarity-preserving. Extensive experiments on three benchmark datasets show that the proposed model outperforms the state-of-the-art methods.","doi":"10.24963/ijcai.2018/332","url":"https://ijcai.org/proceedings/2018/332","pdf":"https://ijcai.org/proceedings/2018/0332.pdf","markdown":"https://ijcai.org/proceedings/2018/332.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/332","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":"Machine Learning","track":"Main track","number":332,"pages":{"first":2397,"last":2403},"keywords":["Machine Learning: Deep Learning","Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation"],"videos":[],"references":[{"position":1,"raw":"[Abadi et al., 2016] Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. 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