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

2004

@inproceedings{ZGH2004,
	vgclass =	{refpap},
	author =	{Xiang Sean Zhou and Ashutosh Garg and Thomas S. Huang},
	title =	{A Discussion of Nonlinear Variants of Biased Discriminants
	for Interactive Image Retrieval},
	booktitle =	{Proceedings of the Third International Conference on Image
	and Video Retrieval (CIVR 2004)},
	address =	{Dublin, Ireland},
	number =	{3115},
	series =	{Lecture Notes in Computer Science},
	pages =	{353--364},
	publisher =	{Springer-Verlag},
	month =	{July~21--23},
	year =	{2004},
	url =	{http://www.springerlink.com/link.asp?id=5k66j791107ql59l},
	abstract =	{During an interactive image retrieval process with
	relevance feedback, kernel-based or boosted learning algorithms can
	provide superior nonlinear modeling capability. In this paper, we
	discuss such nonlinear extensions for biased discriminants, or BiasMap
	[1, 2]. Kernel partial alignment is proposed as the criterion for
	kernel selection. The associated analysis also provides a gauge on
	relative class scatters, which can guide an asymmetric learner, such as
	BiasMap, toward better class modeling. We also propose two boosted
	versions of BiasMap. Unlike existing approach that boosts feature
	components or vectors to form a composite classifier, our scheme boosts
	linear BiasMap toward a nonlinear ranker which is more suited for
	small-sample learning during interactive image retrieval. Experiments
	on heterogeneous image database retrieval as well as small sample face
	retrieval are used for performance evaluations.},
}