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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
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
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