Clustering and classifying images with local and global variability
AbstractA 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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Bibliographic InfoPaper provided by Universidad Carlos III, Departamento de Estadística y Econometría in its series Statistics and Econometrics Working Papers with number ws090101.
Date of creation: Jan 2009
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More information through EDIRC
Images; Cluster; Classification;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-01-17 (All new papers)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- 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.
- Mónica Benito & Daniel Peña, 2004. "Dimensionality Reduction With Image Data," Statistics and Econometrics Working Papers ws041003, Universidad Carlos III, Departamento de Estadística y Econometría.
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