{"type":"ScholarlyArticle","title":"Supervised Short-Length Hashing","authors":[{"name":"Xingbo Liu","slug":"xingbo-liu","given":"Xingbo","family":"Liu","affiliation":"School of Computer Science and Technology , Shandong University , Jinan , P.R. China"},{"name":"Xiushan Nie","slug":"xiushan-nie","given":"Xiushan","family":"Nie","affiliation":"School of Computer Science and Technology , Shandong Jianzhu University , Jinan , P.R. China"},{"name":"Quan Zhou","slug":"quan-zhou","given":"Quan","family":"Zhou","affiliation":"School of Software , Shandong University , Jinan , P.R. China"},{"name":"Xiaoming Xi","slug":"xiaoming-xi","given":"Xiaoming","family":"Xi","affiliation":"School of Computer Science and Technology , Shandong Jianzhu University , Jinan , P.R. China"},{"name":"Lei Zhu","slug":"lei-zhu","given":"Lei","family":"Zhu","affiliation":"School of Information Science and Engineering , Shandong Normal University , Jinan , P.R. China"},{"name":"Yilong Yin","slug":"yilong-yin","given":"Yilong","family":"Yin","affiliation":"School of Software , Shandong University , Jinan , P.R. China"}],"abstract":"Hashing can compress high-dimensional data into compact binary codes, while preserving the similarity, to facilitate efficient retrieval and storage.\r\nHowever, when retrieving using an extremely short length hash code learned by the existing methods, the performance cannot be guaranteed because of\r\nsevere information loss. To address this issue, in this study, we propose a novel supervised short-length hashing (SSLH). In this proposed SSLH, mutual reconstruction between the short-length hash codes and original features are performed to reduce semantic loss. Furthermore, to enhance the robustness\r\nand accuracy of the hash representation, a robust estimator term is added to fully utilize the label information. Extensive experiments conducted on four\r\nimage benchmarks demonstrate the superior performance of the proposed SSLH with short-length hash codes. In addition, the proposed SSLH outperforms\r\nthe existing methods, with long-length hash codes. To the best of our knowledge, this is the first linear-based hashing method that focuses on both short and long-length hash codes for maintaining high precision.","doi":"10.24963/ijcai.2019/420","url":"https://ijcai.org/proceedings/2019/420","pdf":"https://ijcai.org/proceedings/2019/0420.pdf","markdown":"https://ijcai.org/proceedings/2019/420.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/420","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 A-L","track":"Main track","number":420,"pages":{"first":3031,"last":3037},"keywords":["Machine Learning Applications: Applications of Supervised Learning","Multidisciplinary Topics and Applications: Information Retrieval","Machine Learning: Classification"],"videos":[],"references":[{"position":1,"raw":"[Chatfield et al., 2014] Ken Chatfield, Karen Simonyan, An- drea Vedaldi, and Andrew Zisserman. 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