Efficient supervised hashing via exploring local and inner data structure | |
He, Shiyuan1; Ye, Guo1; Hu, Mengqiu1; Yang, Yang1; Shen, Fumin1; Shen, Heng Tao1; Li, Xuelong2; Yang, Yang (dlyyang@gmail.com) | |
2017 | |
会议名称 | 28th Australasian Database Conference, ADC 2017 |
会议录名称 | Databases Theory and Applications - 28th Australasian Database Conference, ADC 2017, Proceedings |
卷号 | 10538 LNCS |
页码 | 98-109 |
会议日期 | 2017-09-25 |
会议地点 | Brisbane, QLD, Australia |
出版者 | Springer Verlag |
产权排序 | 2 |
摘要 | Recent years have witnessed the promising capacity of hashing techniques in tackling nearest neighbor search because of the high efficiency in storage and retrieval. Data-independent approaches (e.g., Locality Sensitive Hashing) normally construct hash functions using random projections, which neglect intrinsic data properties. To compensate this drawback, learning-based approaches propose to explore local data structure and/or supervised information for boosting hashing performance. However, due to the construction of Laplacian matrix, existing methods usually suffer from the unaffordable training cost. In this paper, we propose a novel supervised hashing scheme, which has the merits of (1) exploring the inherent neighborhoods of samples; (2) significantly saving training cost confronted with massive training data by employing approximate anchor graph; as well as (3) preserving semantic similarity by leveraging pair-wise supervised knowledge. Besides, we integrate discrete constraint to significantly eliminate accumulated errors in learning reliable hash codes and hash functions. We devise an alternative algorithm to efficiently solve the optimization problem. Extensive experiments on two image datasets demonstrate that our proposed method is superior to the state-of-the-arts. © 2017, Springer International Publishing AG. |
作者部门 | 光学影像学习与分析中心 |
DOI | 10.1007/978-3-319-68155-9_8 |
收录类别 | EI ; CPCI |
ISBN号 | 9783319681542 |
语种 | 英语 |
ISSN号 | 03029743 |
引用统计 | |
文献类型 | 会议论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/29407 |
专题 | 光谱成像技术研究室 |
通讯作者 | Yang, Yang (dlyyang@gmail.com) |
作者单位 | 1.School of Computer Science and Engineering, Center for Future Media, University of Electronic Science and Technology of China, Chengdu, China 2.State Key Laboratory of Transient Optics and Photonics, Center for OPTical IMagery Analysis and Learning (OPTIMAL), Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Beijing, China |
推荐引用方式 GB/T 7714 | He, Shiyuan,Ye, Guo,Hu, Mengqiu,et al. Efficient supervised hashing via exploring local and inner data structure[C]:Springer Verlag,2017:98-109. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Efficient supervised(439KB) | 会议论文 | 限制开放 | CC BY-NC-SA | 请求全文 |
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