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  • Adaptive hash retrieval with kernel based similarity

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    ZhouPUB3377.pdf (1.416Mb)
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    Accepted Manuscript (AM)
    Author(s)
    Bai, Xiao
    Yan, Cheng
    Yang, Haichuan
    Bai, Lu
    Zhou, Jun
    Hancock, Edwin Robert
    Griffith University Author(s)
    Zhou, Jun
    Year published
    2018
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    Abstract
    Indexing methods have been widely used for fast data retrieval on large scale datasets. When the data are represented by high dimensional vectors, hashing is often used as an efficient solution for approximate similarity search. When a retrieval task does not involve supervised training data, most hashing methods aim at preserving data similarity defined by a distance metric on the feature vectors. Hash codes generated by these approaches normally maintain the Hamming distance of the data in accordance with the similarity function, but ignore the local details of the distribution of data. This objective is not suitable ...
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    Indexing methods have been widely used for fast data retrieval on large scale datasets. When the data are represented by high dimensional vectors, hashing is often used as an efficient solution for approximate similarity search. When a retrieval task does not involve supervised training data, most hashing methods aim at preserving data similarity defined by a distance metric on the feature vectors. Hash codes generated by these approaches normally maintain the Hamming distance of the data in accordance with the similarity function, but ignore the local details of the distribution of data. This objective is not suitable for k-nearest neighbor search since the similarity to the nearest neighbors can vary significantly for different data samples. In this paper, we present a novel adaptive similarity measure which is consistent with k-nearest neighbor search, and prove that it leads to a valid kernel if the original similarity function is a kernel function. Next we propose a method which calculates hash codes using the kernel function. With a low-rank approximation, our hashing framework is more effective than existing methods that preserve similarity over an arbitrary kernel. The proposed similarity function, hashing framework, and their combination demonstrate significant improvement when compared with several alternative state-of-the-art methods.
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    Journal Title
    Pattern Recognition
    DOI
    https://doi.org/10.1016/j.patcog.2017.03.020
    Copyright Statement
    © 2017 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Note
    This publication has been entered into Griffith Research Online as an Advanced Online Version.
    Subject
    Artificial intelligence
    Publication URI
    http://hdl.handle.net/10072/339568
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    • Journal articles

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