Learning binary codes with local and inner data structure | |
He, Shiyuan1; Ye, Guo1; Hu, Mengqiu1; Yang, Yang1; Shen, Fumin1; Shen, Heng Tao1; Li, Xuelong2 | |
作者部门 | 光学影像学习与分析中心 |
2018-03-22 | |
发表期刊 | NEUROCOMPUTING |
ISSN | 0925-2312 |
卷号 | 282页码:32-41 |
产权排序 | 2 |
摘要 | Recent years have witnessed the promising capacity of hashing techniques in tackling nearest neighbor search because of the high efficiency in storage and retrieval. Data-independent approaches (e.g., Locality Sensitive Hashing) normally construct hash functions using random projections, which neglect intrinsic data properties. To compensate this drawback, learning-based approaches propose to explore local data structure and/or supervised information for boosting hashing performance. However, due to the construction of Laplacian matrix, existing methods usually suffer from the unaffordable training cost. In this paper, we propose a novel supervised hashing scheme, which has the merits of (1) exploring the inherent neighborhoods of samples; (2) significantly saving training cost confronted with massive training data by employing approximate anchor graph; as well as (3) preserving semantic similarity by leveraging pair-wise supervised knowledge. Besides, we integrate discrete constraint to significantly eliminate accumulated errors in learning reliable hash codes and hash functions. We devise an alternative algorithm to efficiently solve the optimization problem. Extensive experiments on various image datasets demonstrate that our proposed method is superior to the state-of-the-arts. (c) 2017 Elsevier B.V. All rights reserved. |
关键词 | Supervised Hashing Anchor Graph Nearest Neighbor Search |
DOI | 10.1016/j.neucom.2017.12.005 |
收录类别 | SCI ; EI |
语种 | 英语 |
WOS记录号 | WOS:000424893200004 |
EI入藏号 | 20180104597751 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/30772 |
专题 | 光谱成像技术研究室 |
作者单位 | 1.Univ Elect Sci & Technol China, Ctr Future Media, Chengdu, Sichuan, Peoples R China; 2.Chinese Acad Sci, Ctr OPTical IMagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China |
推荐引用方式 GB/T 7714 | He, Shiyuan,Ye, Guo,Hu, Mengqiu,et al. Learning binary codes with local and inner data structure[J]. NEUROCOMPUTING,2018,282:32-41. |
APA | He, Shiyuan.,Ye, Guo.,Hu, Mengqiu.,Yang, Yang.,Shen, Fumin.,...&Li, Xuelong.(2018).Learning binary codes with local and inner data structure.NEUROCOMPUTING,282,32-41. |
MLA | He, Shiyuan,et al."Learning binary codes with local and inner data structure".NEUROCOMPUTING 282(2018):32-41. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Learning binary code(1147KB) | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 请求全文 |
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