Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/84278
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Type: | Conference paper |
Title: | Fast supervised hashing with decision trees for high-dimensional data |
Author: | Lin, G. Shen, C. Shi, Q. Van Den Hengel, A. Suter, D. |
Citation: | Proceedings, 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, 24-27 June 2014, Columbus, Ohio, USA / pp.1963-1970 |
Publisher: | IEEE |
Issue Date: | 2014 |
Series/Report no.: | IEEE Conference on Computer Vision and Pattern Recognition |
ISBN: | 9781479951178 |
ISSN: | 1063-6919 |
Conference Name: | IEEE Conference on Computer Vision and Pattern Recognition (2014 : Columbus, Ohio) |
Statement of Responsibility: | Guosheng Lin, Chunhua Shen, Qinfeng Shi, Anton van den Hengel, David Suter |
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. |
Rights: | © 2014 IEEE |
DOI: | 10.1109/CVPR.2014.253 |
Description (link): | http://www.pamitc.org/cvpr14/ |
Published version: | http://www.cv-foundation.org/openaccess/content_cvpr_2014/html/Lin_Fast_Supervised_Hashing_2014_CVPR_paper.html |
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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