标题:Sparse manifold embedded hashing for multimedia retrieval
作者:Wang, Yongxin ;Luo, Xin ;Zhang, Huaxiang ;Xu, Xin-Shun
通讯作者:Xu, XinShun
作者机构:[Wang, Yongxin ;Luo, Xin ;Xu, Xin-Shun ] School of Software, Shandong University, Jinan, China;[Zhang, Huaxiang ] School of Information Science and En 更多
会议名称:35th IEEE International Conference on Data Engineering Workshops, ICDEW 2019
会议日期:8 April 2019 through 12 April 2019
来源:Proceedings - 2019 IEEE 35th International Conference on Data Engineering Workshops, ICDEW 2019
出版年:2019
页码:312-318
DOI:10.1109/ICDEW.2019.00011
关键词:Hashing; Manifold embedded hashing; Multimedia retrieval; Sparse-based hashing
摘要:Hashing has become more and more attractive in the large-scale multimedia retrieval community, due to its fast search speed and low storage cost. Most hashing methods focus on finding the inherent data structure, and neglect the sparse reconstruction relationship. Besides, most of them adopt a two-step solution for the structure embedding and the hash codes learning, which may yield suboptimal results. To address these issues, in this paper, we present a novel sparsity-based hashing method, namely, Sparse Manifold embedded hASHing, SMASH for short. It employs the sparse representation technique to extract the implicit structure in the data, and preserves the structure by minimizing the reconstruction error and the quantization loss with constraints to satisfy the independence and balance of the hash codes. An alternative algorithm is devised to solve the optimization problem in SMASH. Based on it, SMASH learns the hash codes and the hash functions simultaneously. Extensive experiments on several benchmark datasets demonstrate that SMASH outperforms some state-of-the-art hashing methods for the multimedia retrieval task. © 2019 IEEE.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069163149&doi=10.1109%2fICDEW.2019.00011&partnerID=40&md5=3e8037897d2e761eaf9d1c14b63aacf5
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