In Defense of Locality-Sensitive Hashing

doi:10.1109/TNNLS.2016.2615085

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 遥感图像处理团队

	In Defense of Locality-Sensitive Hashing
	Ding, Kun1,2 ; Huo, Chunlei1 ; Fan, Bin1 ; Xiang, Shiming1 ; Pan, Chunhong1 ; Fan B(樊斌)
刊名	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
	2018
卷号	29 期号:1 页码:87-103
关键词	Locality-sensitive Hashing (Lsh) Semantic Similarity Search Two-step Hashing
DOI	10.1109/TNNLS.2016.2615085
文献子类	Article
英文摘要	Hashing-based semantic similarity search is becoming increasingly important for building large-scale content-based retrieval system. The state-of-the-art supervised hashing techniques use flexible two-step strategy to learn hash functions. The first step learns binary codes for training data by solving binary optimization problems with millions of variables, thus usually requiring intensive computations. Despite simplicity and efficiency, locality-sensitive hashing (LSH) has never been recognized as a good way to generate such codes due to its poor performance in traditional approximate neighbor search. We claim in this paper that the true merit of LSH lies in transforming the semantic labels to obtain the binary codes, resulting in an effective and efficient two-step hashing framework. Specifically, we developed the locality-sensitive two-step hashing (LS-TSH) that generates the binary codes through LSH rather than any complex optimization technique. Theoretically, with proper assumption, LS-TSH is actually a useful LSH scheme, so that it preserves the label-based semantic similarity and possesses sublinear query complexity for hash lookup. Experimentally, LS-TSH could obtain comparable retrieval accuracy with state of the arts with two to three orders of magnitudes faster training speed.
WOS关键词	NEAREST-NEIGHBOR ; IMAGE RETRIEVAL ; KERNEL METHODS ; RECOGNITION ; MULTICLASS ; REGRESSION ; CODES ; CLASSIFICATION ; REPRESENTATION ; QUANTIZATION
WOS研究方向	Computer Science ; Engineering
语种	英语
WOS记录号	WOS:000419558900008
资助机构	National Natural Science Foundation of China(61375024 ; Strategic Priority Research Program of CAS(XDB02060009) ; Beijing Natural Science Foundation(4162064) ; Priority Academic Program Development of Jiangsu Higher Education Institutions ; Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology ; 61672098 ; 61573352 ; 91338202)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/12323]
专题	自动化研究所_模式识别国家重点实验室_遥感图像处理团队
通讯作者	Fan B(樊斌)
作者单位	1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Ding, Kun,Huo, Chunlei,Fan, Bin,et al. In Defense of Locality-Sensitive Hashing[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2018,29(1):87-103.
APA	Ding, Kun,Huo, Chunlei,Fan, Bin,Xiang, Shiming,Pan, Chunhong,&樊斌.(2018).In Defense of Locality-Sensitive Hashing.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,29(1),87-103.
MLA	Ding, Kun,et al."In Defense of Locality-Sensitive Hashing".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 29.1(2018):87-103.