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A New and Fast Method of Image Indexing

  • Lina-Huang
  • Zhijing-Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4223)

Abstract

Traditional indexing methods face the difficulty of“curse of dimensionality” at high dimensionality. In this paper, the traditional vector approximation is improved. Firstly, it decreases the dimensions by LLE (locally-linear-embedding). As a result of it, a set of absolute low dimensions are gotten. Then, this paper uses the Gaussian mixture distribution and estimates the distribution through EM (expectation-maximization) method. The original data vectors are replaced by vector approximation. This approach gains higher efficiency and less run time. The experiments show a remarkable reduction of I/O. They also show an improvement on the indexing performance and then speed the image retrieval.

Keywords

Locally Linear Embedding Vector Approximation Linear Embedding Piecewise Linear Curve Reconstruction Weight 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Roweis, S.T., Saul, L.K.: Nonlinear Dimensionality Reduction by Locally Linear Embedding. ScientistGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Lina-Huang
    • 1
  • Zhijing-Liu
    • 1
  1. 1.Dept of Computer Science Xidian UniversityXi’an

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