%%% -*-BibTeX-*-
@inproceedings{CVPR14Lin,
    author      = {Guosheng Lin and Chunhua Shen and Qinfeng Shi and Anton {van den Hengel} and David Suter},
    title       = {Fast Supervised Hashing with Decision Trees for High-Dimensional Data},
    year        = {2014},
    month       = {},
    booktitle   = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR'14)},
    address     = {Columbus, Ohio, USA},
    venue       = {CVPR},
    pages       = {},
    volume      = {},
    publisher   = {},
    note        = {},
    eprint      = {1404.1561},
    url         = {https://bitbucket.org/chhshen/fasthash/src},
    pdf         = {},
    project     = {https://bitbucket.org/chhshen/fasthash/},
    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 the 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 perfor- mance 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 func- tions 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.
    },
}