caa a22 4500 60544675X CHVBK 20210128100128.0 cr unu---uuuuu 210128e20150101xx s 000 0 eng 10.1007/s11042-014-1977-3 doi (NATIONALLICENCE)springer-10.1007/s11042-014-1977-3 Memory efficient large-scale image-based localization [Elektronische Daten] [Guoyu Lu, Nicu Sebe, Congfu Xu, Chandra Kambhamettu] 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. Springer Science+Business Media New York, 2014 Image-based localization nationallicence Large scale imagery nationallicence SIFT nationallicence Hamming descriptor nationallicence Dimensionality reduction nationallicence Lu Guoyu Video/Image Modeling and Synthesis Lab, University of Delaware, 19711, Newark, DE, USA aut Sebe Nicu Department of Information Engineering and Computer Science, University of Trento, 38100, Trento, Italy aut Xu Congfu Institute of Artificial Intelligence, Zhejiang University, 310027, Hangzhou, People's Republic of China aut Kambhamettu Chandra Video/Image Modeling and Synthesis Lab, University of Delaware, 19711, Newark, DE, USA aut Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/2(2015-01-01), 479-503 1380-7501 74:2<479 2015 74 11042 https://doi.org/10.1007/s11042-014-1977-3 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11042-014-1977-3 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Lu Guoyu Video/Image Modeling and Synthesis Lab, University of Delaware, 19711, Newark, DE, USA aut NATIONALLICENCE 700 1- Sebe Nicu Department of Information Engineering and Computer Science, University of Trento, 38100, Trento, Italy aut NATIONALLICENCE 700 1- Xu Congfu Institute of Artificial Intelligence, Zhejiang University, 310027, Hangzhou, People's Republic of China aut NATIONALLICENCE 700 1- Kambhamettu Chandra Video/Image Modeling and Synthesis Lab, University of Delaware, 19711, Newark, DE, USA aut NATIONALLICENCE 773 0- Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/2(2015-01-01), 479-503 1380-7501 74:2<479 2015 74 11042