Compact Structure Hashing via Sparse and Similarity Preserving Embedding
文献类型：期刊论文
| 作者 | Ye, Renzhen1,2; Li, Xuelong1
|
| 刊名 | ieee transactions on cybernetics
 |
| 出版日期 | 2016-03-01 |
| 卷号 | 46期号:3页码:718-729 |
| 关键词 | Hashing nearest neighbor search structure sparse-based hashing |
| ISSN号 | 2168-2267 |
| 产权排序 | 1 |
| 通讯作者 | ye, rz |
| 英文摘要 | over the past few years, fast approximate nearest neighbor (ann) search is desirable or essential, e.g., in huge databases, and therefore many hashing-based ann techniques have been presented to return the nearest neighbors of a given query from huge databases. hashing-based ann techniques have become popular due to its low memory cost and good computational complexity. recently, most of hashing methods have realized the importance of the relationship of the data and exploited the different structure of data to improve retrieval performance. however, a limitation of the aforementioned methods is that the sparse reconstructive relationship of the data is neglected. in this case, few methods can find the discriminating power and own the local properties of the data for learning compact and effective hash codes. to take this crucial issue into account, this paper proposes a method named special structure-based hashing (ssbh). ssbh can preserve the underlying geometric information among the data, and exploit the prior information that there exists sparse reconstructive relationship of the data, for learning compact and effective hash codes. upon extensive experimental results, ssbh is demonstrated to be more robust and more effective than state-of-the-art hashing methods. |
| WOS标题词 | science & technology ; technology |
| 学科主题 | computer science, artificial intelligence ; computer science, cybernetics |
| 类目[WOS] | computer science, artificial intelligence ; computer science, cybernetics |
| 研究领域[WOS] | computer science |
| 关键词[WOS] | image superresolution ; search ; trees |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000370963500012 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/27855]  |
| 专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
| 作者单位 | 1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr Opt Imagery Anal & Learning, Xian 710119, Peoples R China 2.Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian 710119, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ye, Renzhen,Li, Xuelong. Compact Structure Hashing via Sparse and Similarity Preserving Embedding[J]. ieee transactions on cybernetics,2016,46(3):718-729. |
| APA | Ye, Renzhen,&Li, Xuelong.(2016).Compact Structure Hashing via Sparse and Similarity Preserving Embedding.ieee transactions on cybernetics,46(3),718-729. |
| MLA | Ye, Renzhen,et al."Compact Structure Hashing via Sparse and Similarity Preserving Embedding".ieee transactions on cybernetics 46.3(2016):718-729. |
入库方式： OAI收割
来源：西安光学精密机械研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。