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

2004

@article{TFS2004,
	vgclass =	{refpap},
	author =	{Hong Tang and Tao Fang and Pengfei Shi},
	title =	{Spectral similarity measure based on fuzzy feature
	contrast model},
	journal =	{Optics Communications},
	volume =	{238},
	number =	{1--3},
	pages =	{123--137},
	month =	{August},
	year =	{2004},
	url =	{http://dx.doi.org/10.1016/j.optcom.2004.04.030},
	abstract =	{In the famous feature contrast model (FCM), the similarity
	measure is a linear combination of the common (similar) features and
	the distinctive (dissimilar) features. Because of the combination, FCM
	is better than other similarity models in explaining human perception
	similarity. However, the feature of FCM is binary. By defining the
	fuzzy feature set, FCM is extended into fuzzy feature contrast model
	(FFCM). In this paper, we adapt FFCM to measure spectral similarity. A
	spectrum is represented as a set including two subsets. The two subsets
	are characterized by spectral reflectance and spectral absorption,
	respectively. Meanwhile, the spectral reflectance and absorption are
	defined as the common (similar) and distinctive (dissimilar) subset in
	spectral set, respectively. Our spectral similarity model is expressed
	as a linear combination of the common subset, distinctive subset and
	their interaction. The difference between our model and FFCM is
	interaction of two subsets is defined. Moreover, kernel principal
	component analysis (KPCA) is used to remove the high correlation among
	different bands before spectral similarity measure. Experiments show
	that our model is effective in spectral similarity measure.},
}