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Fast Localization in Large-Scale Environments Using Supervised Indexing of Binary Features
Feng, Youji1,2; Fan, Lixin3; Wu, Yihong1
AbstractThe 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.
KeywordImage Based Localization Binary Feature Approximate Nearest Neighbor Search
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
Funding OrganizationNokia Research(LF14011659182) ; National Basic Research Program of China(2012CB316302) ; National Natural Science Foundation of China(61421004)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000366558900006
Citation statistics
Cited Times:15[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.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
Recommended Citation
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.
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