Refereed full papers (journals, book chapters, international conferences)

2004

@article{BGA2004,
	vgclass =	{refpap},
	author =	{Le Borgne, Herv\'{e} and Anne Gu\'{e}rin-Dugu\'{e} and
	Anestis Antoniadis},
	title =	{Representation of images for classification with
	independent features},
	journal =	{Pattern Recognition Letters},
	volume =	{25},
	number =	{2},
	pages =	{141--154},
	month =	{January},
	year =	{2004},
	url =	{http://dx.doi.org/10.1016/j.patrec.2003.09.011},
	abstract =	{In this study, independent component analysis (ICA) is
	used to compute features extracted from natural images. The use of ICA
	is justified in the context of classification of natural images for two
	reasons. On the one hand the model of image suggests that the
	underlying statistical principles may be the same as those that
	determine the structure of the visual cortex. As a consequence, the
	filters that ICA produces are adapted to the statistics of natural
	images. On the other hand, we adopt a non-parametric approach that
	require density estimation in many dimensions, and independence between
	features appears as a solution to overthrow the ``curse of
	dimensionality''. Hence we introduce several signatures of natural
	images that use these feature, and we define some similarity measures
	that correspond to these signatures. These signatures appear as more
	and more accurate estimations of densities, and the associated
	distances as estimations of the Kullback-Leibler divergence between
	the densities. Efficiency of the couple signature/distance is estimated
	by a K-nearest-neighbour classifier, with a ``leave-one-out'' procedure
	for all the signatures we define, and a ``bootstrap'' based one for the
	best results.},
}