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Weakly Principal Component Hashing with Multiple Tables

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Book cover Advances in Multimedia Modeling

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7733))

Abstract

Image hashing based Approximate Nearest Neighbor (ANN) searching has drawn much attention in large-scale image dataset application, where balance the precision and high recall rate is difficulty task. In this paper, we propose a weakly principal component hash method with multiple tables to encode binary codes. Analyzing the distribution of projected data on different principal component directions, we find that neighbors which are far in some principal component directions maybe near in the other directions. Therefore, we construct multiple-table hashing to search the missed positive samples by previous tables, which can improve the recall. For each table, we project data to different principal component directions to learn hashing functions. In order to improve the precision rate, neighborhood points in Euclidean space should also be neighborhoods in Hamming space. So we optimize the projected data using orthogonal matrix to preserve the structure of the data in the Hamming space. Experimental and compared with six hashing results on public datasets show that the proposed method is more effective and outperforms the state-of-the-art.

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Author information

Authors and Affiliations

  1. School of Information and Communication Engineering, Dalian University of Technology, Dalian, China, 116023

    Haiyan Fu, Xiangwei Kong, Yanqing Guo & Jiayin Lu

Authors
  1. Haiyan Fu
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  2. Xiangwei Kong
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  3. Yanqing Guo
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  4. Jiayin Lu
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Editor information

Editors and Affiliations

  1. Microsoft Research Asia, 5 Danling Street, 100080, Beijing, China

    Shipeng Li  & Tao Mei  & 

  2. School of Electrical Engineering and Computer Science, University of Ottawa, 800 King Edward, K1N 6N5, Ottawa, ON, Canada

    Abdulmotaleb El Saddik

  3. School of Computer and Information, Hefei University of Technology, Road Tunxi 193#, 230009, Hefei, Anhui, China

    Meng Wang  & Richang Hong  & 

  4. Department of Information Engineering and Computer Science, University of Trento, ommarive 14, 38100, Trento, Italy

    Nicu Sebe

  5. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117583, Singapore, Singapore

    Shuicheng Yan

  6. School of Computing, CLARITY: Centre for Sensor Web Technologies, Dublin City University, Glasnevin, 9, Dublin, Ireland

    Cathal Gurrin

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© 2013 Springer-Verlag Berlin Heidelberg

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Fu, H., Kong, X., Guo, Y., Lu, J. (2013). Weakly Principal Component Hashing with Multiple Tables. In: Li, S., et al. Advances in Multimedia Modeling. Lecture Notes in Computer Science, vol 7733. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35728-2_28

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  • DOI: https://doi.org/10.1007/978-3-642-35728-2_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35727-5

  • Online ISBN: 978-3-642-35728-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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