%%% -*-BibTeX-*-
@inproceedings{ICCV13Lin,
    author      = {Guosheng Lin and Chunhua Shen and David Suter and Anton {van den Hengel}},
    title       = {A General Two-step Approach to Learning-Based Hashing},
    year        = {2013},
    month       = {December},
    booktitle   = {IEEE International Conference on Computer Vision (ICCV'13)},
    address     = {Sydney, Australia},
    venue       = {ICCV},
    pages       = {},
    volume      = {},
    publisher   = {},
    note        = {},
    eprint      = {1309.1853},
    url         = {},
    pdf         = {},
    project     = {https://bitbucket.org/guosheng/two-step-hashing/},
    abstract    = {
            Most existing approaches to hashing apply a single form of
            hash function, and an optimization process which is typically deeply
            coupled to this specific form. This tight coupling restricts the
            flexibility of the method to respond to the data, and can result in
            complex optimization problems that are difficult to solve. Here we
            propose a flexible yet simple framework that is able to accommodate
            different types of loss functions and hash functions. This
            framework allows a number of existing approaches to hashing to be
            placed in context, and simplifies the development of new
            problem-specific hashing methods. Our framework decomposes hashing
            learning problem into two steps: hash bit learning and hash
            function learning based on the learned bits. The first step can
            typically be formulated as binary quadratic problems, and the
            second step can be accomplished by training standard binary
            classifiers. Both problems have been extensively studied in the
            literature.
    },
}
 

@inproceedings{ICCV13Li,
    author      = {Xi Li and Yao Li and Chunhua Shen and Anthony Dick and Anton {van den Hengel}},
    title       = {Contextual Hypergraph Modeling for Salient Object Detection},
    year        = {2013},
    month       = {December},
    booktitle   = {IEEE International  Conference on Computer Vision (ICCV'13)},
    address     = {Sydney, Australia},
    venue       = {ICCV},
    pages       = {},
    volume      = {},
    publisher   = {},
    note        = {},
    eprint      = {1310.5767},
    url         = {},
    pdf         = {},
    project     = {https://bitbucket.org/chhshen/saliency-detection},
    abstract    = {Salient object detection aims to locate objects that
        capture human attention within images.
    Previous approaches often pose this as a problem of image contrast analysis.
    In this work, we model an image as a hypergraph that
    utilizes a set of hyperedges to capture the contextual
    properties of image pixels or regions. As a result,
    the problem of salient object detection becomes one of finding salient
    vertices and hyperedges in the hypergraph.
    The main advantage of hypergraph modeling is that it takes into
    account each pixel's (or region's) affinity with its neighborhood
    as well as its separation from image background.
    Furthermore, we propose an alternative approach based on
    center-versus-surround contextual contrast analysis,
    which performs salient object detection by optimizing a cost-sensitive
    support vector machine (SVM) objective function.
    Experimental results on four challenging datasets demonstrate the
    effectiveness of the proposed approaches against the state-of-the-art
    approaches to salient object detection.},
}