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Clustering and classifying images with local and global variability

  • Andrea Giuliodori


  • Rosa E. Lillo


  • Daniel Peña


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    A procedure for clustering and classifying images determined by three classification variables is presented. A measure of global variability based on the singular value decomposition of the image matrices, and two average measures of local variability based on spatial correlation and spatial changes. The performance of the procedure is compared using three different databases.

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    Paper provided by Universidad Carlos III, Departamento de Estadística y Econometría in its series Statistics and Econometrics Working Papers with number ws090101.

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    Date of creation: Jan 2009
    Date of revision:
    Handle: RePEc:cte:wsrepe:ws090101
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    1. Marron, J.S. & Todd, Michael J. & Ahn, Jeongyoun, 2007. "Distance-Weighted Discrimination," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1267-1271, December.
    2. Mónica Benito & Daniel Peña, 2004. "Dimensionality Reduction With Image Data," Statistics and Econometrics Working Papers ws041003, Universidad Carlos III, Departamento de Estadística y Econometría.
    3. Peña, Daniel & Rodríguez, Julio, 2003. "Descriptive measures of multivariate scatter and linear dependence," Journal of Multivariate Analysis, Elsevier, vol. 85(2), pages 361-374, May.
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