Fast Localization in Large-Scale Environments Using Supervised Indexing of Binary Features
文献类型：期刊论文
| 作者 | Feng, Youji1,2; Fan, Lixin3; Wu, Yihong1
|
| 刊名 | IEEE TRANSACTIONS ON IMAGE PROCESSING
 |
| 出版日期 | 2016 |
| 卷号 | 25期号:1页码:343-358 |
| 关键词 | Image based localization binary feature approximate nearest neighbor search |
| 英文摘要 | The essence of image-based localization lies in matching 2D key points in the query image and 3D points in the database. State-of-the-art methods mostly employ sophisticated key point detectors and feature descriptors, e.g., Difference of Gaussian (DoG) and Scale Invariant Feature Transform (SIFT), to ensure robust matching. While a high registration rate is attained, the registration speed is impeded by the expensive key point detection and the descriptor extraction. In this paper, we propose to use efficient key point detectors along with binary feature descriptors, since the extraction of such binary features is extremely fast. The naive usage of binary features, however, does not lend itself to significant speedup of localization, since existing indexing approaches, such as hierarchical clustering trees and locality sensitive hashing, are not efficient enough in indexing binary features and matching binary features turns out to be much slower than matching SIFT features. To overcome this, we propose a much more efficient indexing approach for approximate nearest neighbor search of binary features. This approach resorts to randomized trees that are constructed in a supervised training process by exploiting the label information derived from that multiple features correspond to a common 3D point. In the tree construction process, node tests are selected in a way such that trees have uniform leaf sizes and low error rates, which are two desired properties for efficient approximate nearest neighbor search. To further improve the search efficiency, a probabilistic priority search strategy is adopted. Apart from the label information, this strategy also uses non-binary pixel intensity differences available in descriptor extraction. By using the proposed indexing approach, matching binary features is no longer much slower but slightly faster than matching SIFT features. Consequently, the overall localization speed is significantly improved due to the much faster key point detection and descriptor extraction. It is empirically demonstrated that the localization speed is improved by an order of magnitude as compared with state-of-the-art methods, while comparable registration rate and localization accuracy are still maintained. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| 研究领域[WOS] | Computer Science ; Engineering |
| 关键词[WOS] | NEAREST-NEIGHBOR SEARCH ; IMAGE ; RECOGNITION ; ALGORITHM |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000366558900006 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/10644]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_机器人视觉团队 |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China 3.Nokia Technol, Tampere 33100, Finland |
| 推荐引用方式 GB/T 7714 | Feng, Youji,Fan, Lixin,Wu, Yihong. Fast Localization in Large-Scale Environments Using Supervised Indexing of Binary Features[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2016,25(1):343-358. |
| APA | Feng, Youji,Fan, Lixin,&Wu, Yihong.(2016).Fast Localization in Large-Scale Environments Using Supervised Indexing of Binary Features.IEEE TRANSACTIONS ON IMAGE PROCESSING,25(1),343-358. |
| MLA | Feng, Youji,et al."Fast Localization in Large-Scale Environments Using Supervised Indexing of Binary Features".IEEE TRANSACTIONS ON IMAGE PROCESSING 25.1(2016):343-358. |
入库方式： OAI收割
来源：自动化研究所
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。