In Defense of Locality-Sensitive Hashing
Ding, Kun1,2; Huo, Chunlei1; Fan, Bin1; Xiang, Shiming1; Pan, Chunhong1; Fan B(樊斌)
2018
发表期刊IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷号29期号:1页码:87-103
文章类型Article
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.
关键词Locality-sensitive Hashing (Lsh) Semantic Similarity Search Two-step Hashing
WOS标题词Science & Technology ; Technology
DOI10.1109/TNNLS.2016.2615085
关键词[WOS]NEAREST-NEIGHBOR ; IMAGE RETRIEVAL ; KERNEL METHODS ; RECOGNITION ; MULTICLASS ; REGRESSION ; CODES ; CLASSIFICATION ; REPRESENTATION ; QUANTIZATION
收录类别SCI
语种英语
项目资助者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)
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号WOS:000419558900008
引用统计
被引频次:1[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/12323
专题模式识别国家重点实验室_先进数据分析与学习
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
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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.
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