• DocumentCode
    2298337
  • Title

    Mahalanobis Distance Metric Based Laplacian Mapping for Image Recognition

  • Author

    Zhang, Xingfu

  • Author_Institution
    Coll. of Comput. Sci. & Technol., Harbin Eng. Univ., Harbin, China
  • fYear
    2010
  • fDate
    1-2 Nov. 2010
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    An Improved algorithm for image recognition, called Mahalanobis Distance Metric based Laplacian Mapping Algorithm(MLMA), is presented in this paper. Firstly MLMA learns a Mahalanobis metric matrix from training samples, then we use the Mahalanobis metic as a similarity measure in Laplacian Mapping Algorithm. Comparison of MLMA and standard Laplacian Mapping Algorithm in ORL and USPS databases proves that MLMA is more effective and robust than standard Laplacian Mapping Algorithm.
  • Keywords
    Laplace equations; image recognition; visual databases; MLMA; Mahalanobis distance metric based Laplacian mapping algorithm; Mahalanobis metric matrix; ORL databases; USPS databases; image recognition; Algorithm design and analysis; Databases; Eigenvalues and eigenfunctions; Image recognition; Laplace equations; Measurement; Vectors; dimensionality reduction; image recognition; laplacian mapping; manifold learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Internet Computing for Science and Engineering (ICICSE), 2010 Fifth International Conference on
  • Conference_Location
    Heilongjiang
  • Print_ISBN
    978-1-4244-9954-0
  • Type

    conf

  • DOI
    10.1109/ICICSE.2010.25
  • Filename
    6076530
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2298337