Knowledge Commons of Institute of Automation,CAS
Efficient Similarity Learning for Asymmetric Hashing | |
Cheng Da1,2; Yang Yang1; Kun Ding1; Chunlei Huo1; Shiming Xiang1,2; Chunhong Pan1 | |
2017 | |
会议名称 | IEEE International Conference on Image Processing |
会议日期 | 2017-9-17 |
会议地点 | Beijing, CHINA |
摘要 | 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. |
收录类别 | EI |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/15384 |
专题 | 多模态人工智能系统全国重点实验室_先进时空数据分析与学习 |
通讯作者 | Chunlei Huo |
作者单位 | 1.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 |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Cheng Da,Yang Yang,Kun Ding,et al. Efficient Similarity Learning for Asymmetric Hashing[C],2017. |
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