Large-Scale Unsupervised Hashing with Shared Structure Learning

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

	Large-Scale Unsupervised Hashing with Shared Structure Learning
	Liu, Xianglong 1; Mu, Yadong 2; Zhang, Danchen 1; Lang, Bo 1; Li, Xuelong3
刊名	ieee transactions on cybernetics
	2015-09-01
卷号	45 期号:9 页码:1811-1822
关键词	Locality sensitive hashing (LSH) nearest neighbor search shared structure learning unsupervised hashing
英文摘要	hashing methods are effective in generating compact binary signatures for images and videos. this paper addresses an important open issue in the literature, i.e., how to learn compact hash codes by enhancing the complementarity among different hash functions. most of prior studies solve this problem either by adopting time-consuming sequential learning algorithms or by generating the hash functions which are subject to some deliberately-designed constraints (e.g., enforcing hash functions orthogonal to one another). we analyze the drawbacks of past works and propose a new solution to this problem. our idea is to decompose the feature space into a subspace shared by all hash functions and its complementary subspace. on one hand, the shared subspace, corresponding to the common structure across different hash functions, conveys most relevant information for the hashing task. similar to data de-noising, irrelevant information is explicitly suppressed during hash function generation. on the other hand, in case that the complementary subspace also contains useful information for specific hash functions, the final form of our proposed hashing scheme is a compromise between these two kinds of subspaces. to make hash functions not only preserve the local neighborhood structure but also capture the global cluster distribution of the whole data, an objective function incorporating spectral embedding loss, binary quantization loss, and shared subspace contribution is introduced to guide the hash function learning. we propose an efficient alternating optimization method to simultaneously learn both the shared structure and the hash functions. experimental results on three well-known benchmarks cifar-10, nus-wide, and a-trecvid demonstrate that our approach significantly outperforms state-of-the-art hashing methods.
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 retrieval ; iterative quantization ; procrustean approach ; binary-codes ; search ; reranking
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000360019000009
公开日期	2015-10-20
内容类型	期刊论文
源URL	[http://ir.opt.ac.cn/handle/181661/25358]
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.AT&T Labs Res, Middletown, NJ 07748 USA 3.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr Opt Imagery Anal & Learning, Xian 710119, Peoples R China
推荐引用方式 GB/T 7714	Liu, Xianglong,Mu, Yadong,Zhang, Danchen,et al. Large-Scale Unsupervised Hashing with Shared Structure Learning[J]. ieee transactions on cybernetics,2015,45(9):1811-1822.
APA	Liu, Xianglong,Mu, Yadong,Zhang, Danchen,Lang, Bo,&Li, Xuelong.(2015).Large-Scale Unsupervised Hashing with Shared Structure Learning.ieee transactions on cybernetics,45(9),1811-1822.
MLA	Liu, Xianglong,et al."Large-Scale Unsupervised Hashing with Shared Structure Learning".ieee transactions on cybernetics 45.9(2015):1811-1822.