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Learning binary codes with local and inner data structure
He, Shiyuan1; Ye, Guo1; Hu, Mengqiu1; Yang, Yang1; Shen, Fumin1; Shen, Heng Tao1; Li, Xuelong2
Department光学影像学习与分析中心
2018-03-22
Source PublicationNEUROCOMPUTING
ISSN0925-2312
Volume282Pages:32-41
Contribution Rank2
Abstract

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.

KeywordSupervised Hashing Anchor Graph Nearest Neighbor Search
DOI10.1016/j.neucom.2017.12.005
Indexed BySCI ; EI
Language英语
WOS IDWOS:000424893200004
EI Accession Number20180104597751
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/30772
Collection光学影像学习与分析中心
Affiliation1.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
Recommended Citation
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.
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