1998
@techreport{Squ1998,
vgclass = {report},
vgproject = {viper,cbir},
author = {David McG. Squire},
title = {Generalization performance of factor analysis techniques
used for image database organization},
number = {98.01},
institution = {Computer Vision Group, Computing Centre, University
of Geneva},
address = {rue G\'{e}n\'{e}ral Dufour, 24, CH-1211 Gen\`{e}ve,
Switzerland},
month = {January},
year = {1998},
url = {/publications/postscript/1998/VGTR98.01_Squire.pdf},
url1 = {/publications/postscript/1998/VGTR98.01_Squire.ps.gz},
abstract = {The goal of this paper is to evaluate the generalization
performance of a variety of factor analysis techniques in an image
database environment. Factor analysis techniques, such as Principal
Components Analysis, have been proposed as means of reducing the
dimensionality of the data stored in image retrieval systems. These
techniques compute a transformation which is applied to vectors of
image features to produce vectors of lower dimensionality which still
characterize the original data well. Computing such transformations for
very large numbers of images is computationally expensive, especially
if this calculation must be repeated each time new images are added to
the database. It is to be hoped, therefore, that a transformation
computed using a subset of all possible images will perform well when
applied to images not used in its derivation. To evaluate this
generalization ability, we measure the agreement between partitionings
of image sets computed using such transformations with those produced
by human subjects.},
}