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

2004

@article{LeL2004,
	vgclass =	{refpap},
	vgproject =	{cbir},
	author =	{Wee Kheng Leow and Rui Li},
	title =	{The analysis and applications of adaptive-binning color
	histograms},
	journal =	{Computer Vision and Image Understanding (special issue on Colour for Image Indexing and Retrieval)},
	volume =	{94},
	number =	{1--3},
	pages =	{67--91},
	month =	{April/June},
	year =	{2004},
	url =	{http://dx.doi.org/10.1016/j.cviu.2003.10.010},
	abstract =	{Histograms are commonly used in content-based image
	retrieval systems to represent the distributions of colors in images.
	It is a common understanding that histograms that adapt to images can
	represent their color distributions more efficiently than do histograms
	with fixed binnings. However, existing systems almost exclusively adopt
	fixed-binning histograms because, among existing well-known
	dissimilarity measures, only the computationally expensive Earth
	Mover's Distance (EMD) can compare histograms with different binnings.
	This paper addresses the issue by defining a new dissimilarity measure
	that is more reliable than the Euclidean distance and yet
	computationally less expensive than EMD. Moreover, a mathematically
	sound definition of mean histogram can be defined for histogram
	clustering applications. Extensive test results show that adaptive
	histograms produce the best overall performance, in terms of good
	accuracy, small number of bins, no empty bin, and efficient
	computation, compared to existing methods for histogram retrieval,
	classification, and clustering tasks.},
}