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

2004

@article{VaZ2004,
	vgclass =	{refpap},
	author =	{Manik Varma and Andrew Zisserman},
	title =	{Unifying statistical texture classification frameworks},
	journal =	{Image and Vision Computing},
	volume =	{22},
	number =	{14},
	pages =	{1175--1183},
	month =	{December},
	year =	{2004},
	url =	{http://dx.doi.org/10.1016/j.imavis.2004.03.012},
	abstract =	{The objective of this paper is to examine statistical
	approaches to the classification of textured materials from a single
	image obtained under unknown viewpoint and illumination. The approaches
	investigated here are based on the joint probability distribution of
	filter responses.

	We review previous work based on this formulation and make two
	observations. First, we show that there is a correspondence between the
	two common representations of filter outputs--textons and binned
	histograms. Second, we show that two classification methodologies,
	nearest neighbour matching and Bayesian classification, are equivalent
	for particular choices of the distance measure. We describe the pros
	and cons of these alternative representations and distance measures,
	and illustrate the discussion by classifying all the materials in the
	Columbia-Utrecht (CUReT) texture database.

	These equivalences allow us to perform direct comparisons between the
	texton frequency matching framework, best exemplified by the
	classifiers of Leung and Malik [IJCV 01], Cula and Dana [CVPR 01], and
	Varma and Zisserman [ECCV 02], and the Bayesian framework most closely
	represented by the work of Konishi and Yuille [CVPR 00].},
}