Compact Structure Hashing via Sparse and Similarity Preserving Embedding

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

	Compact Structure Hashing via Sparse and Similarity Preserving Embedding
	Ye, Renzhen 1,2; Li, Xuelong1
刊名	ieee transactions on cybernetics
	2016-03-01
卷号	46 期号:3 页码:718-729
关键词	Hashing nearest neighbor search structure sparse-based hashing
ISSN号	2168-2267
通讯作者	ye, rz
产权排序	1
英文摘要	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.
学科主题	computer science, artificial intelligence ; computer science, cybernetics
WOS标题词	science & technology ; technology
类目[WOS]	computer science, artificial intelligence ; computer science, cybernetics
研究领域[WOS]	computer science
关键词[WOS]	image superresolution ; search ; trees
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000370963500012
内容类型	期刊论文
源URL	[http://ir.opt.ac.cn/handle/181661/27855]
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.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. Compact Structure Hashing via Sparse and Similarity Preserving Embedding[J]. ieee transactions on cybernetics,2016,46(3):718-729.
APA	Ye, Renzhen,&Li, Xuelong.(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.