Clustering and classifying images with local and global variability
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.
|Date of creation:||Jan 2009|
|Date of revision:|
|Contact details of provider:|| Postal: |
Web page: http://portal.uc3m.es/portal/page/portal/dpto_estadistica
More information through EDIRC
References listed on IDEAS
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.:
- 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.
- 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.
When requesting a correction, please mention this item's handle: RePEc:cte:wsrepe:ws090101. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ()
If references are entirely missing, you can add them using this form.