{"type":"ScholarlyArticle","title":"MLS3RDUH: Deep Unsupervised Hashing via Manifold based Local Semantic Similarity Structure Reconstructing","authors":[{"name":"Rong-Cheng Tu","slug":"rong-cheng-tu","given":"Rong-Cheng","family":"Tu","affiliation":"Department of Computer Science and Technology , Beijing Institute of Technology , China; CETC Big Data Research Institute Co., Ltd. , Guiyang 55002 , China"},{"name":"Xian-Ling Mao","slug":"xian-ling-mao","given":"Xian-Ling","family":"Mao","affiliation":"Department of Computer Science and Technology , Beijing Institute of Technology , China; Zhijiang Lab , Hangzhou , China"},{"name":"Wei Wei","slug":"wei-wei","given":"Wei","family":"Wei","affiliation":"School of Computer Science , Huazhong University of Science and Technology , China"}],"abstract":"Most of the unsupervised hashing methods usually map images into semantic similarity-preserving hash codes by constructing local semantic similarity structure as guiding information, i.e., treating each point similar to its k nearest neighbours. However, for an image, some of its k nearest neighbours may be dissimilar to it, i.e., they are noisy datapoints which will damage the retrieval performance. Thus, to tackle this problem, in this paper, we propose a novel deep unsupervised hashing method, called MLS3RDUH, which can reduce the noisy datapoints to further enhance retrieval performance. Specifically, the proposed method first defines a novel similarity matrix by utilising the intrinsic manifold structure in feature space and the cosine similarity of datapoints to reconstruct the local semantic similarity structure. Then a novel log-cosh hashing loss function is used to optimize the hashing network to generate compact hash codes by incorporating the defined similarity as guiding information. Extensive experiments on three public datasets show that the proposed method outperforms the state-of-the-art baselines.","doi":"10.24963/ijcai.2020/479","url":"https://ijcai.org/proceedings/2020/479","pdf":"https://ijcai.org/proceedings/2020/0479.pdf","markdown":"https://ijcai.org/proceedings/2020/479.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/479","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Multidisciplinary Topics and Applications","track":"Main track","number":479,"pages":{"first":3466,"last":3472},"keywords":["Machine Learning: Unsupervised Learning","Machine Learning: Deep Learning","Multidisciplinary Topics and Applications: Information Retrieval"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24282/file_24282.m3u8","length_sec":261,"provider":"hls"}],"references":[{"position":1,"raw":"[Cao et al., 2017] Zhangjie Cao, Mingsheng Long, Jianmin Wang, and Philip S Yu. 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