Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/84278
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dc.contributor.author | Lin, G. | - |
dc.contributor.author | Shen, C. | - |
dc.contributor.author | Shi, Q. | - |
dc.contributor.author | Van Den Hengel, A. | - |
dc.contributor.author | Suter, D. | - |
dc.date.issued | 2014 | - |
dc.identifier.citation | Proceedings, 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, 24-27 June 2014, Columbus, Ohio, USA / pp.1963-1970 | - |
dc.identifier.isbn | 9781479951178 | - |
dc.identifier.issn | 1063-6919 | - |
dc.identifier.uri | http://hdl.handle.net/2440/84278 | - |
dc.description.abstract | Supervised hashing aims to map the original features to compact binary codes that are able to preserve label based similarity in the Hamming space. Non-linear hash functions have demonstrated their advantage over linear ones due to their powerful generalization capability. In the literature, kernel functions are typically used to achieve non-linearity in hashing, which achieve encouraging retrieval performance at the price of slow evaluation and training time. Here we propose to use boosted decision trees for achieving non-linearity in hashing, which are fast to train and evaluate, hence more suitable for hashing with high dimensional data. In our approach, we first propose sub-modular formulations for the hashing binary code inference problem and an efficient GraphCut based block search method for solving large-scale inference. Then we learn hash functions by training boosted decision trees to fit the binary codes. Experiments demonstrate that our proposed method significantly outperforms most state-of-the-art methods in retrieval precision and training time. Especially for highdimensional data, our method is orders of magnitude faster than many methods in terms of training time. | - |
dc.description.statementofresponsibility | Guosheng Lin, Chunhua Shen, Qinfeng Shi, Anton van den Hengel, David Suter | - |
dc.description.uri | http://www.pamitc.org/cvpr14/ | - |
dc.language.iso | en | - |
dc.publisher | IEEE | - |
dc.relation.ispartofseries | IEEE Conference on Computer Vision and Pattern Recognition | - |
dc.rights | © 2014 IEEE | - |
dc.source.uri | http://www.cv-foundation.org/openaccess/content_cvpr_2014/html/Lin_Fast_Supervised_Hashing_2014_CVPR_paper.html | - |
dc.title | Fast supervised hashing with decision trees for high-dimensional data | - |
dc.type | Conference paper | - |
dc.contributor.conference | IEEE Conference on Computer Vision and Pattern Recognition (2014 : Columbus, Ohio) | - |
dc.identifier.doi | 10.1109/CVPR.2014.253 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Shi, Q. [0000-0002-9126-2107] | - |
dc.identifier.orcid | Van Den Hengel, A. [0000-0003-3027-8364] | - |
dc.identifier.orcid | Suter, D. [0000-0001-6306-3023] | - |
Appears in Collections: | Aurora harvest 4 Computer Science publications |
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RA_hdl_84278.pdf | Restricted Access | 440.25 kB | Adobe PDF | View/Open |
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