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Technical Reports


  • @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,
    	month =	{January},
    	year =	{1998},
    	url =	{/publications/postscript/1998/VGTR98.01_Squire.pdf},
    	url1 =	{/publications/postscript/1998/},
    	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.},