CASIA OpenIR  > 模式识别国家重点实验室  > 先进数据分析与学习
EFFICIENT SIMILARITY LEARNING FOR ASYMMETRIC HASHING
Cheng Da1,2; Yang Yang1; Kun Ding1; Chunlei Huo1; Shiming Xiang1; Chunhong Pan1
2017
Conference NameIEEE International Conference on Image Processing
Conference Date2017-9-17
Conference PlaceBeijing, CHINA
Abstract
Hashing techniques with asymmetric schemes (e.g., only binarizing the database points) have recently attracted wide attention in the circle of image retrieval. In comparison with those methods which binarize simultaneously both of the query and database points, they not only enjoy the storage and search efficiencies, but also provide higher accuracy.
Gearing to this line, this paper proposes a metric-embedded asymmetric hashing (MEAH) that learns jointly a bilinear similarity measure and binary codes of database points in an unsupervised manner. Technically, the learned similarity measure is able to bridge the gap between the binary codes and the real-valued codes, which are represented possibly
with different dimensions. What is more, this measure is capable of preserving the global structure hidden in the database. Extensive experiments on two public image benchmarks
demonstrate the superiority of our approach over the several state-of-the-art unsupervised hashing methods.
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15384
Collection模式识别国家重点实验室_先进数据分析与学习
Corresponding AuthorChunlei Huo
Affiliation1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
2.School of Computer and Control Engineering, University of Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Cheng Da,Yang Yang,Kun Ding,et al. EFFICIENT SIMILARITY LEARNING FOR ASYMMETRIC HASHING[C],2017.
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