Learning Discriminative Binary Codes for Large-scale Cross-modal Retrieval | |
Xu, Xing1; Shen, Fumin1; Yang, Yang1; Shen, Heng Tao1; Li, Xuelong2; Shen, Fumin (fumin.shen@gmail.com) | |
作者部门 | 光学影像学习与分析中心 |
2017-05-01 | |
发表期刊 | IEEE TRANSACTIONS ON IMAGE PROCESSING |
ISSN | 1057-7149 |
卷号 | 26期号:5页码:2494-2507 |
产权排序 | 2 |
摘要 | Hashing based methods have attracted considerable attention for efficient cross-modal retrieval on large-scale multimedia data. The core problem of cross-modal hashing is how to learn compact binary codes that construct the underlying correlations between heterogeneous features from different modalities. A majority of recent approaches aim at learning hash functions to preserve the pairwise similarities defined by given class labels. However, these methods fail to explicitly explore the discriminative property of class labels during hash function learning. In addition, they usually discard the discrete constraints imposed on the to-be-learned binary codes, and compromise to solve a relaxed problem with quantization to obtain the approximate binary solution. Therefore, the binary codes generated by these methods are suboptimal and less discriminative to different classes. To overcome these drawbacks, we propose a novel cross-modal hashing method, termed discrete cross-modal hashing (DCH), which directly learns discriminative binary codes while retaining the discrete constraints. Specifically, DCH learns modality-specific hash functions for generating unified binary codes, and these binary codes are viewed as representative features for discriminative classification with class labels. An effective discrete optimization algorithm is developed for DCH to jointly learn the modality-specific hash function and the unified binary codes. Extensive experiments on three benchmark data sets highlight the superiority of DCH under various cross-modal scenarios and show its state-of-the-art performance. |
文章类型 | Article |
关键词 | Cross-modal Retrieval Hashing Discrete Optimization Discriminant Analysis |
WOS标题词 | Science & Technology ; Technology |
DOI | 10.1109/TIP.2017.2676345 |
收录类别 | SCI ; EI |
关键词[WOS] | IMAGE RETRIEVAL ; SEMANTICS ; SEARCH ; SPACE |
语种 | 英语 |
WOS研究方向 | Computer Science ; Engineering |
项目资助者 | National Natural Science Foundation of China(61602089 ; National Thousand-Young-Talents Program of China ; Fundamental Research Funds for the Central Universities(ZYGX2014Z007 ; 61502081 ; ZYGX2015J055 ; 61572108 ; ZYGX2016KYQD114) ; 61632007 ; 61472063) |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000399396400031 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/28861 |
专题 | 光谱成像技术研究室 |
通讯作者 | Shen, Fumin (fumin.shen@gmail.com) |
作者单位 | 1.Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 610051, Peoples R China 2.Chinese Acad Sci, Ctr OPT IMagery Anal & Learning, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Xian 710119, Peoples R China |
推荐引用方式 GB/T 7714 | Xu, Xing,Shen, Fumin,Yang, Yang,et al. Learning Discriminative Binary Codes for Large-scale Cross-modal Retrieval[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2017,26(5):2494-2507. |
APA | Xu, Xing,Shen, Fumin,Yang, Yang,Shen, Heng Tao,Li, Xuelong,&Shen, Fumin .(2017).Learning Discriminative Binary Codes for Large-scale Cross-modal Retrieval.IEEE TRANSACTIONS ON IMAGE PROCESSING,26(5),2494-2507. |
MLA | Xu, Xing,et al."Learning Discriminative Binary Codes for Large-scale Cross-modal Retrieval".IEEE TRANSACTIONS ON IMAGE PROCESSING 26.5(2017):2494-2507. |
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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 | ||
Learning Discriminat(2126KB) | 期刊论文 | 作者接受稿 | 限制开放 | CC BY-NC-SA | 请求全文 |
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