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

2000

@article{SLH2000,
	vgclass =	{refpap},
	author =	{Nicu Sebe and Michael S. Lew and Dionysius P. Huijsmans},
	title =	{Toward improved ranking metrics},
	journal =	{IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume =	{22},
	number =	{10},
	pages =	{1132--1143},
	month =	{October},
	year =	{2000},
	url =	{http://ieeexplore.ieee.org/iel5/34/19035/00879793.pdf},
	abstract =	{In many computer vision algorithms, a metric or
	similarity measure is used to determine the distance between two
	features. The Euclidean or SSD (sum of the squared differences) metric
	is prevalent and justified from a maximum likelihood perspective when
	the additive noise distribution is Gaussian. Based on real noise
	distributions measured from international test sets, we have found that
	the Gaussian noise distribution assumption is often invalid. This
	implies that other metrics, which have distributions closer to the real
	noise distribution, should be used. In this paper, we consider three
	different applications: content-based retrieval in image databases,
	stereo matching, and motion tracking. In each of them, we experiment
	with different modeling functions for the noise distribution and
	compute the accuracy of the methods using the corresponding distance
	measures. In our experiments, we compared the SSD metric, the SAD (sum
	of the absolute differences) metric, the Cauchy metric, and the
	Kullback relative information. For several algorithms from the research
	literature which used the SSD or SAD, we showed that greater accuracy
	could be obtained by using the Cauchy metric instead.},
}