Learning binary codes with local and inner data structure

doi:10.1016/j.neucom.2017.12.005

CORC > 西安光学精密机械研究所 > 中国科学院西安光学精密机械研究所 > 光学影像学习与分析中心

	Learning binary codes with local and inner data structure
	He, Shiyuan 1; Ye, Guo 1; Hu, Mengqiu 1; Yang, Yang 1; Shen, Fumin 1; Shen, Heng Tao 1; Li, Xuelong2
刊名	NEUROCOMPUTING
	2018-03-22
卷号	282 页码:32-41
关键词	Supervised Hashing Anchor Graph Nearest Neighbor Search
ISSN号	0925-2312
DOI	10.1016/j.neucom.2017.12.005
产权排序	2
英文摘要	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.
语种	英语
WOS记录号	WOS:000424893200004
内容类型	期刊论文
源URL	[http://ir.opt.ac.cn/handle/181661/30772]
专题	西安光学精密机械研究所_光学影像学习与分析中心
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.