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NOKMeans: Non-Orthogonal K-means Hashing

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Book cover Computer Vision – ACCV 2014 (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9003))

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Abstract

Finding nearest neighbor points in a large scale high dimensional data set is of wide interest in computer vision. One popular and efficient approach is to encode each data point as a binary code in Hamming space using separating hyperplanes. One condition which is often implicitly assumed is that the separating hyperplanes should be mutually orthogonal. With the aim of increasing the representation capability of the hyperplanes when used for indexing, we relax the orthogonality assumption without forsaking the alternate view of using cluster centers to represent the indexing partitions. This is achieved by viewing the data points in a space determined by their distances to the hyperplanes. We show that the proposed method is superior to existing state-of-the-art techniques on several large computer vision datasets.

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Authors and Affiliations

  1. Department of Computer Science, University of Otago, Dunedin, New Zealand

    Xiping Fu, Brendan McCane, Steven Mills & Michael Albert

Authors
  1. Xiping Fu
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  2. Brendan McCane
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  3. Steven Mills
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  4. Michael Albert
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Corresponding author

Correspondence to Xiping Fu .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Bayern, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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Fu, X., McCane, B., Mills, S., Albert, M. (2015). NOKMeans: Non-Orthogonal K-means Hashing. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision – ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9003. Springer, Cham. https://doi.org/10.1007/978-3-319-16865-4_11

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  • DOI: https://doi.org/10.1007/978-3-319-16865-4_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16864-7

  • Online ISBN: 978-3-319-16865-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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