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Hashing with dual complementary projection learning for fast image retrieval

文献类型：期刊论文

作者Li, Peng; Cheng, Jian; Lu, Hanqing
刊名NEUROCOMPUTING
出版日期2013-11-23
卷号120页码:83-89
关键词Hashing Complementary projection learning Binary codes Fast image retrieval
英文摘要Due to explosive growth of visual content on the web, there is an emerging need of fast similarity search to efficiently exploit such enormous web contents from very large databases. Recently, hashing has become very popular for efficient nearest neighbor search in large scale applications. However, many traditional hashing methods learn the binary codes in a single shot or only employ a single hash table, thus they usually cannot achieve both high precision and recall simultaneously. In this paper, we propose a novel dual complementary hashing (DCH) approach to learn the codes with multiple hash tables. In our method, not only the projection for each bit inside a hash table has the property of error-correcting but also the different hash tables complement each other. Therefore, the binary codes learned by our approach are more powerful for fast similarity search. Extensive experiments on publicly available datasets demonstrate the effectiveness of our approach. (c) 2013 Elsevier B.V. All rights reserved.
WOS标题词Science & Technology ; Technology
类目[WOS]Computer Science, Artificial Intelligence
研究领域[WOS]Computer Science
关键词[WOS]APPROXIMATE NEAREST-NEIGHBOR ; ALGORITHM ; DIMENSIONS
收录类别SCI
语种英语
WOS记录号WOS:000324847100010
源URL[http://ir.ia.ac.cn/handle/173211/3366]  
专题自动化研究所_模式识别国家重点实验室_图像与视频分析团队
作者单位Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Li, Peng,Cheng, Jian,Lu, Hanqing. Hashing with dual complementary projection learning for fast image retrieval[J]. NEUROCOMPUTING,2013,120:83-89.
APA Li, Peng,Cheng, Jian,&Lu, Hanqing.(2013).Hashing with dual complementary projection learning for fast image retrieval.NEUROCOMPUTING,120,83-89.
MLA Li, Peng,et al."Hashing with dual complementary projection learning for fast image retrieval".NEUROCOMPUTING 120(2013):83-89.

入库方式： OAI收割

来源：自动化研究所

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