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  • Isometric Mapping Hashing

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    ZhouPUB5.pdf (603.7Kb)
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    Accepted Manuscript (AM)
    Author(s)
    Liu, Yanzhen
    Bai, Xiao
    Yang, Haichuan
    Zhou, Jun
    Zhang, Zhihong
    Griffith University Author(s)
    Zhou, Jun
    Year published
    2015
    Metadata
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    Abstract
    Hashing is a popular solution to Approximate Nearest Neighbor (ANN) problems. Many hashing schemes aim at preserving the Euclidean distance of the original data. However, it is the geodesic distance rather than the Euclidean distance that more accurately characterizes the semantic similarity of data, especially in a high dimensional space. Consequently, manifold based hashing methods have achieved higher accuracy than conventional hashing schemes. To compute the geodesic distance, one should construct a nearest neighbor graph and invoke the shortest path algorithm, which is too expensive for a retrieval task. In this paper, ...
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    Hashing is a popular solution to Approximate Nearest Neighbor (ANN) problems. Many hashing schemes aim at preserving the Euclidean distance of the original data. However, it is the geodesic distance rather than the Euclidean distance that more accurately characterizes the semantic similarity of data, especially in a high dimensional space. Consequently, manifold based hashing methods have achieved higher accuracy than conventional hashing schemes. To compute the geodesic distance, one should construct a nearest neighbor graph and invoke the shortest path algorithm, which is too expensive for a retrieval task. In this paper, we present a hashing scheme that preserves the geodesic distance and use a feasible out-of-sample method to generate the binary codes efficiently. The experiments show that our method outperforms several alternative hashing methods.
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    Conference Title
    Graph-based Representations in Pattern Recognition
    Publisher URI
    http://www.nlpr.ia.ac.cn/gbr2015/
    DOI
    https://doi.org/10.1007/978-3-319-18224-7_32
    Copyright Statement
    © 2015 Springer Berlin/Heidelberg. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.com
    Subject
    Computer vision
    Publication URI
    http://hdl.handle.net/10072/69690
    Collection
    • Conference outputs

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