PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Simple Feature Extraction for High Dimensional Image Representations
Christian Savu-Krohn and Peter Auer
In: Subspace, Latent Structure and Feature Selection techniques: Statistical and Optimisation perspectives, 23-25 Feb 2005, Bohinj, Slovenia.


We investigate a method to find local clusters in low dimensional subspaces of high dimensional data, e.g. in high dimensional image descriptions. Using cluster centers instead of the full set of data will speed up the performance of learning algorithms for object recognition, and migh t also improve performance because overfitting is avoided. Usingthe Graz01 database, our method outperforms the current standard method for feature extraction from high dimensional image respresentations.

EPrint Type:Conference or Workshop Item (Paper)
Additional Information:accepted for publication
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:1501
Deposited By:Christian Savu-Krohn
Deposited On:28 November 2005