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Local Linear Spectral Hashing

  • Kang Zhao
  • Dengxiang Liu
  • Hongtao Lu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8228)

Abstract

Hashing for large scale image retrieval has become more and more popular because of its improvement in computational speed and storage reduction. Spectral Hashing (SH) is a very efficient unsupervised hashing method through mapping similar images to similar binary codes. However, it doesn’t take the non-neighbor points into consideration, and its assumption of uniform data distribution is usually not true. In this paper, we propose a \(local\ linear\ spectral\ hashing\) framework that minimizes the average Hamming distance with a new local neighbor matrix, which can guarantee the mapping not only from neighbor images to neighbor codes, but also from non-neighbor images to non-neighbor codes. Based on the framework, we utilize three linear methods to handle the proposed problem, including orthogonal hashing, non-orthogonal hashing, and sequential hashing. The experiments on two huge datasets demonstrate the efficiency and accuracy of our methods.

Keywords

Image Retrieval Hamming Distance Spectral Hashing Eigenvalue Decomposition 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Kang Zhao
    • 1
  • Dengxiang Liu
    • 1
  • Hongtao Lu
    • 1
  1. 1.MOE-Microsoft Laboratory for Intelligent Computing and Intelligent Systems Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghaiP.R. China

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