Compact Structure Hashing via Sparse and Similarity Preserving Embedding

doi:10.1109/TCYB.2015.2414299

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	Compact Structure Hashing via Sparse and Similarity Preserving Embedding
	Ye, Renzhen 1,2; Li, Xuelong1 ; Ye, RZ
作者部门	光学影像学习与分析中心
	2016-03-01
发表期刊	IEEE TRANSACTIONS ON CYBERNETICS
ISSN	2168-2267
卷号	46 期号:3 页码:718-729
产权排序	1
摘要	Over the past few years, fast approximate nearest neighbor (ANN) search is desirable or essential, e.g., in huge databases, and therefore many hashing-based ANN techniques have been presented to return the nearest neighbors of a given query from huge databases. Hashing-based ANN techniques have become popular due to its low memory cost and good computational complexity. Recently, most of hashing methods have realized the importance of the relationship of the data and exploited the different structure of data to improve retrieval performance. However, a limitation of the aforementioned methods is that the sparse reconstructive relationship of the data is neglected. In this case, few methods can find the discriminating power and own the local properties of the data for learning compact and effective hash codes. To take this crucial issue into account, this paper proposes a method named special structure-based hashing (SSBH). SSBH can preserve the underlying geometric information among the data, and exploit the prior information that there exists sparse reconstructive relationship of the data, for learning compact and effective hash codes. Upon extensive experimental results, SSBH is demonstrated to be more robust and more effective than state-of-the-art hashing methods.
文章类型	Article
关键词	Hashing Nearest Neighbor Search Structure Sparse-based Hashing
学科领域	Computer Science, Artificial Intelligence
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TCYB.2015.2414299
收录类别	SCI ; EI
关键词[WOS]	IMAGE SUPERRESOLUTION ; SEARCH ; TREES
语种	英语
WOS研究方向	Computer Science
项目资助者	National Basic Research Program of China (973 Program)(2012CB316400) ; National Natural Science Foundation of China(61125106 ; Chinese Academy of Sciences(KGZD-EW-T03) ; 61300142)
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号	WOS:000370963500012
引用统计	被引频次：32[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.opt.ac.cn/handle/181661/27855
专题	光谱成像技术研究室
通讯作者	Ye, RZ
作者单位	1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr Opt Imagery Anal & Learning, Xian 710119, Peoples R China 2.Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian 710119, Peoples R China
推荐引用方式 GB/T 7714	Ye, Renzhen,Li, Xuelong,Ye, RZ. Compact Structure Hashing via Sparse and Similarity Preserving Embedding[J]. IEEE TRANSACTIONS ON CYBERNETICS,2016,46(3):718-729.
APA	Ye, Renzhen,Li, Xuelong,&Ye, RZ.(2016).Compact Structure Hashing via Sparse and Similarity Preserving Embedding.IEEE TRANSACTIONS ON CYBERNETICS,46(3),718-729.
MLA	Ye, Renzhen,et al."Compact Structure Hashing via Sparse and Similarity Preserving Embedding".IEEE TRANSACTIONS ON CYBERNETICS 46.3(2016):718-729.

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