CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Consensus hashing
Leng, Cong; Cheng, Jian
Source PublicationMACHINE LEARNING
2015-09-01
Volume100Issue:2-3Pages:379-398
SubtypeArticle
AbstractHashing techniques have been widely used in many machine learning applications because of their efficiency in both computation and storage. Although a variety of hashing methods have been proposed, most of them make some implicit assumptions about the statistical or geometrical structure of data. In fact, few hashing algorithms can adequately handle all kinds of data with different structures. When considering hybrid structure datasets, different hashing algorithms might produce different and possibly inconsistent binary codes. Inspired by the successes of classifier combination and clustering ensembles, in this paper, we present a novel combination strategy for multiple hashing results, named consensus hashing. By defining the measure of consensus of two hashing results, we put forward a simple yet effective model to learn consensus hash functions which generate binary codes consistent with the existing ones. Extensive experiments on several large scale benchmarks demonstrate the overall superiority of the proposed method compared with state-of-the-art hashing algorithms.
WOS HeadingsScience & Technology ; Technology
DOI10.1007/s10994-015-5496-x
WOS KeywordSEARCH
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000359747100009
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/8910
Collection模式识别国家重点实验室_图像与视频分析
AffiliationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Leng, Cong,Cheng, Jian. Consensus hashing[J]. MACHINE LEARNING,2015,100(2-3):379-398.
APA Leng, Cong,&Cheng, Jian.(2015).Consensus hashing.MACHINE LEARNING,100(2-3),379-398.
MLA Leng, Cong,et al."Consensus hashing".MACHINE LEARNING 100.2-3(2015):379-398.
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