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

2004

@article{HaC2004,
	vgclass =	{refpap},
	author =	{Michael Edberg Hansen and Jens Michael Carstensen},
	title =	{Density-based retrieval from high-similarity image
	databases},
	journal =	{Pattern Recognition},
	year =	{2004},
	note =	{(in press)},
	url =	{http://dx.doi.org/10.1016/j.patcog.2004.02.018},
	abstract =	{Many image classification problems can fruitfully be
	thought of as image retrieval in a "high similarity image database"
	(HSID) characterized by being tuned towards a specific application and
	having a high degree of visual similarity between entries that should
	be distinguished. We introduce a method for HSID retrieval using a
	similarity measure based on a linear combination of Jeffreys-Matusita
	distances between distributions of local (pixelwise) features estimated
	from a set of automatically and consistently defined image regions. The
	weight coefficients are estimated based on optimal retrieval
	performance. Experimental results on the difficult task of visually
	identifying clones of fungal colonies grown in a petri dish and
	categorization of pelts show a high retrieval accuracy of the method
	when combined with standardized sample preparation and image
	acquisition.},
}