Query-Adaptive Reciprocal Hash Tables for Nearest Neighbor Search

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

	Query-Adaptive Reciprocal Hash Tables for Nearest Neighbor Search
	Liu, Xianglong 1; Deng, Cheng 2; Lang, Bo 1; Tao, Dacheng3 ; Li, Xuelong4
刊名	ieee transactions on image processing
	2016-02-01
卷号	25 期号:2 页码:907-919
关键词	Locality sensitive hashing bit selection complementary hash tables query adaptive nearest neighbor search
ISSN号	1057-7149
通讯作者	deng, c
产权排序	4
英文摘要	recent years have witnessed the success of binary hashing techniques in approximate nearest neighbor search. in practice, multiple hash tables are usually built using hashing to cover more desired results in the hit buckets of each table. however, rare work studies the unified approach to constructing multiple informative hash tables using any type of hashing algorithms. meanwhile, for multiple table search, it also lacks of a generic query-adaptive and fine-grained ranking scheme that can alleviate the binary quantization loss suffered in the standard hashing techniques. to solve the above problems, in this paper, we first regard the table construction as a selection problem over a set of candidate hash functions. with the graph representation of the function set, we propose an efficient solution that sequentially applies normalized dominant set to finding the most informative and independent hash functions for each table. to further reduce the redundancy between tables, we explore the reciprocal hash tables in a boosting manner, where the hash function graph is updated with high weights emphasized on the misclassified neighbor pairs of previous hash tables. to refine the ranking of the retrieved buckets within a certain hamming radius from the query, we propose a query-adaptive bitwise weighting scheme to enable fine-grained bucket ranking in each hash table, exploiting the discriminative power of its hash functions and their complement for nearest neighbor search. moreover, we integrate such scheme into the multiple table search using a fast, yet reciprocal table lookup algorithm within the adaptive weighted hamming radius. in this paper, both the construction method and the query-adaptive search method are general and compatible with different types of hashing algorithms using different feature spaces and/or parameter settings. our extensive experiments on several large-scale benchmarks demonstrate that the proposed techniques can significantly outperform both the naive construction methods and the state-of-the-art hashing algorithms.
学科主题	computer science, artificial intelligence ; engineering, electrical & electronic
WOS标题词	science & technology ; technology
类目[WOS]	computer science, artificial intelligence ; engineering, electrical & electronic
研究领域[WOS]	computer science ; engineering
关键词[WOS]	code ranking ; quantization
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000368938400003
内容类型	期刊论文
源URL	[http://ir.opt.ac.cn/handle/181661/27799]
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.Beihang Univ, State Key Lab Software Dev Environm, Beijing 100191, Peoples R China 2.Xidian Univ, Sch Elect Engn, Xian 710071, Peoples R China 3.Univ Technol Sydney, Ctr Quantum Computat & Intelligent Syst, Fac Engn & Informat Technol, Sydney, NSW 2007, Australia 4.Chinese Acad Sci, State Key Lab Transient Opt & Photon, Ctr Opt Imagery Anal & Learning, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China
推荐引用方式 GB/T 7714	Liu, Xianglong,Deng, Cheng,Lang, Bo,et al. Query-Adaptive Reciprocal Hash Tables for Nearest Neighbor Search[J]. ieee transactions on image processing,2016,25(2):907-919.
APA	Liu, Xianglong,Deng, Cheng,Lang, Bo,Tao, Dacheng,&Li, Xuelong.(2016).Query-Adaptive Reciprocal Hash Tables for Nearest Neighbor Search.ieee transactions on image processing,25(2),907-919.
MLA	Liu, Xianglong,et al."Query-Adaptive Reciprocal Hash Tables for Nearest Neighbor Search".ieee transactions on image processing 25.2(2016):907-919.