{"type":"ScholarlyArticle","title":"Discriminative Deep Hashing for Scalable Face Image Retrieval","authors":[{"name":"Jie Lin","slug":"jie-lin","given":"Jie","family":"Lin","affiliation":"School of Computer Science and Engineering , Nanjing University of Science and Technology"},{"name":"Zechao Li","slug":"zechao-li","given":"Zechao","family":"Li","affiliation":"School of Computer Science and Engineering , Nanjing University of Science and Technology"},{"name":"Jinhui Tang","slug":"jinhui-tang","given":"Jinhui","family":"Tang","affiliation":"School of Computer Science and Engineering , Nanjing University of Science and Technology"}],"abstract":"With the explosive growth of images containing faces, scalable face image retrieval has attracted increasing attention. Due to the amazing effectiveness, deep hashing has become a popular hashing method recently. In this work, we propose a new Discriminative Deep Hashing (DDH) network to learn discriminative and compact hash codes for large-scale face image retrieval. The proposed network incorporates the end-to-end learning, the divide-and-encode module and the desired discrete code learning into a unified framework. Specifically, a network with a stack of convolution-pooling layers is proposed to extract multi-scale and robust features by merging the outputs of the third max pooling layer and the fourth convolutional layer. To reduce the redundancy among hash codes and the network parameters simultaneously, a divide-and-encode module to generate compact hash codes. Moreover, a loss function is introduced to minimize the prediction errors of the learned hash codes, which can lead to discriminative hash codes. Extensive experiments on two datasets demonstrate that the proposed method achieves superior performance compared with some state-of-the-art hashing methods.","doi":"10.24963/ijcai.2017/315","url":"https://ijcai.org/proceedings/2017/315","pdf":"https://ijcai.org/proceedings/2017/0315.pdf","markdown":"https://ijcai.org/proceedings/2017/315.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/315","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 A-R","track":"Main track","number":315,"pages":{"first":2266,"last":2272},"keywords":["Machine Learning: Classification","Machine Learning: Machine Learning","Machine Learning: Deep Learning","Machine Learning: Neural Networks"],"videos":[],"references":[{"position":1,"raw":"[Andoni and Indyk, 2006] Alexandr Andoni and Piotr Indyk. 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