NDLI: Memory efficient large-scale image-based localization

Content Provider	Springer Nature Link
Author	Lu, Guoyu Sebe, Nicu Xu, Congfu Kambhamettu, Chandra
Copyright Year	2014
Abstract	Local features have been widely used in the area of image-based localization. However, large-scale 2D-to-3D matching problems still involve massive memory consumption, which is mainly caused by the high dimensionality of the features (e.g. 128 dimensions of SIFT feature). This paper introduces a new method that decreases local features’ high dimensionality for reducing memory capacity and accelerating the descriptor matching process. With this new method, all descriptors are projected into a lower dimensional space through the new learned matrices that are able to reduce the curse of dimensionality in the large scale image-based localization. The low dimensional descriptors are then mapped into a Hamming space for further reducing the memory requirement. This study also proposes an image-based localization pipeline based on the new learned Hamming descriptors. The new learned descriptor and the localization pipeline are applied to two challenging datasets. The experimental results show that the proposed method achieves extraordinary image registration performance compared with the published results from state-of-the-art methods.
Starting Page	479
Ending Page	503
Page Count	25
File Format	PDF
ISSN	13807501
Journal	Multimedia Tools and Applications
Volume Number	74
Issue Number	2
e-ISSN	15737721
Language	English
Publisher	Springer US
Publisher Date	2014-05-08
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Image-based localization Large scale imagery SIFT Hamming descriptor Dimensionality reduction Multimedia Information Systems Computer Communication Networks Data Structures, Cryptology and Information Theory Special Purpose and Application-Based Systems
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Hardware and Architecture Software Media Technology

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