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

2004

@article{Hei2004,
	vgclass =	{refpap},
	vgproject =	{cbir},
	author =	{Gunther Heidemann},
	title =	{Combining spatial and colour information for content based
	image retrieval},
	journal =	{Computer Vision and Image Understanding (special issue on Colour for Image Indexing and Retrieval)},
	volume =	{94},
	number =	{1--3},
	pages =	{234--270},
	month =	{April/June},
	year =	{2004},
	url =	{http://dx.doi.org/10.1016/j.cviu.2003.10.009},
	abstract =	{Colour is one of the most important features in content
	based image retrieval. However, colour is rarely used as a feature that
	codes local spatial information, except for colour texture. This paper
	presents an approach to represent spatial colour distributions using
	local principal component analysis (PCA). The representation is based
	on image windows which are selected by two complementary data driven
	attentive mechanisms: a symmetry based saliency map and an edge and
	corner detector. The eigenvectors obtained from local PCA of the
	selected windows form colour patterns that capture both low and high
	spatial frequencies, so they are well suited for shape as well as
	texture representation. Projections of the windows selected from the
	image database to the local PCs serve as a compact representation for
	the search database. Queries are formulated by specifying windows
	within query images. System feedback makes both the search process and
	the results comprehensible for the user.},
}