Fast Supervised Hashing with Decision Trees for High-Dimensional Data

Guosheng Lin, Chunhua Shen, Qinfeng Shi, Anton van den Hengel, David Suter; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 1963-1970

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 high-dimensional data, our method is orders of magnitude faster than many methods in terms of training time.

Related Material


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[bibtex]
@InProceedings{Lin_2014_CVPR,
author = {Lin, Guosheng and Shen, Chunhua and Shi, Qinfeng and van den Hengel, Anton and Suter, David},
title = {Fast Supervised Hashing with Decision Trees for High-Dimensional Data},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2014}
}