{"type":"ScholarlyArticle","title":"Semi-Supervised Deep Hashing with a Bipartite Graph","authors":[{"name":"Xinyu Yan","slug":"xinyu-yan","given":"Xinyu","family":"Yan","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University , Nanjing 210023 , China"},{"name":"Lijun Zhang","slug":"lijun-zhang","given":"Lijun","family":"Zhang","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University , Nanjing 210023 , China"},{"name":"Wu-Jun Li","slug":"wu-jun-li","given":"Wu-Jun","family":"Li","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University , Nanjing 210023 , China"}],"abstract":"Recently, deep learning has been successfully applied to the problem of hashing, yielding remarkable performance compared to traditional methods with hand-crafted features. However, most of existing deep hashing methods are designed for the supervised scenario and require a large number of labeled data. In this paper, we propose a novel semi-supervised hashing method for image retrieval, named Deep Hashing with a Bipartite Graph (DHBG), to simultaneously learn embeddings, features and hash codes. More specifically, we construct a bipartite graph to discover the underlying structure of data, based on which an embedding is generated for each instance. Then, we feed raw pixels as well as embeddings to a deep neural network, and concatenate the resulting features to determine the hash code. Compared to existing methods, DHBG is a universal framework that is able to utilize various types of graphs and losses. Furthermore, we propose an inductive variant of DHBG to support out-of-sample extensions. Experimental results on real datasets show that our DHBG outperforms state-of-the-art hashing methods.","doi":"10.24963/ijcai.2017/452","url":"https://ijcai.org/proceedings/2017/452","pdf":"https://ijcai.org/proceedings/2017/0452.pdf","markdown":"https://ijcai.org/proceedings/2017/452.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/452","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":452,"pages":{"first":3238,"last":3244},"keywords":["Machine Learning: Deep Learning","Machine Learning: Semi-Supervised Learning","Machine Learning: Machine Learning"],"videos":[],"references":[{"position":1,"raw":"[Bottou, 2010] Léon Bottou. 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