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

2004

@article{NHC2004,
	vgclass =	{refpap},
	author =	{Huzefa Neemuchwala and Alfred Hero and Paul Carson},
	title =	{Image matching using alpha-entropy measures and entropic
	graphs},
	journal =	{Signal Processing},
	year =	{2004},
	note =	{(in press)},
	url =	{http://dx.doi.org/10.1016/j.sigpro.2004.10.002},
	abstract =	{Matching a reference image to a secondary image extracted
	from a database of transformed exemplars constitutes an important image
	retrieval task. Two related problems are: specification of a general
	class of discriminatory image features and an appropriate similarity
	measure to rank the closeness of the query to the database. In this
	paper we present a general method based on matching high dimensional
	image features, using entropic similarity measures that can be
	empirically estimated using entropic graphs such as the minimal
	spanning tree (MST). The entropic measures we consider are
	generalizations of the well-known Kullback-Liebler (KL) distance, the
	mutual information (MI) measure, and the Jensen difference. Our
	entropic graph approach has the advantage of being implementable for
	high dimensional feature spaces for which other entropy-based pattern
	matching methods are computationally difficult. We compare our
	technique to previous entropy matching methods for a variety of
	continuous and discrete features sets including: single pixel gray
	levels; tag sub-image features; and independent component analysis
	(ICA) features. We illustrate the methodology for multimodal face
	retrieval and ultrasound (US) breast image registration.},
}