{"type":"ScholarlyArticle","title":"Object Detection based Deep Unsupervised Hashing","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 , 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 , China"},{"name":"Bo-Si Feng","slug":"bo-si-feng","given":"Bo-Si","family":"Feng","affiliation":"Department of Computer Science and Technology , Beijing Institute of Technology , China"},{"name":"Shu-ying Yu","slug":"shu-ying-yu","given":"Shu-ying","family":"Yu","affiliation":"Department of Computer Science and Technology , Beijing Institute of Technology , China"}],"abstract":"Recently, similarity-preserving hashing methods have been extensively studied for large-scale image retrieval. Compared with unsupervised hashing, supervised hashing methods for labeled data have usually better performance by utilizing semantic label information. Intuitively, for unlabeled data, it will improve the performance of unsupervised hashing methods if we can first mine some supervised semantic 'label information' from unlabeled data and then incorporate the 'label information' into the training process. Thus, in this paper, we propose a novel Object Detection based Deep Unsupervised Hashing method (ODDUH). Specifically, a pre-trained object detection model is utilized to mining supervised 'label information', which is used to guide the learning process to generate high-quality hash codes. Extensive experiments on two public datasets demonstrate that the proposed method outperforms the state-of-the-art unsupervised hashing methods in the image retrieval task.","doi":"10.24963/ijcai.2019/500","url":"https://ijcai.org/proceedings/2019/500","pdf":"https://ijcai.org/proceedings/2019/0500.pdf","markdown":"https://ijcai.org/proceedings/2019/500.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/500","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning M-Z","track":"Main track","number":500,"pages":{"first":3606,"last":3612},"keywords":["Machine Learning Applications: Applications of Unsupervised Learning","Machine Learning: Unsupervised Learning","Multidisciplinary Topics and Applications: Information Retrieval"],"videos":[],"references":[{"position":1,"raw":"[Allan and Verbeek, 2009] Moray Allan and Jakob Verbeek. 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