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

1999

@article{PBQ1999,
	vgclass =	{refpap},
	vgproject =	{cbir},
	author =	{Jing Peng and Bir Bhanu and Shan Qing},
	title =	{Probabilistic Feature Relevance Learning for Content-Based
	Image Retrieval},
	journal =	{Computer Vision and Image Understanding (special issue on content-based access for image
	and video libraries)},
	volume =	{75},
	number =	{1/2},
	pages =	{150--164},
	month =	{July/August},
	year =	{1999},
	abstract =	{Most of the current image retrieval systems use
	``one-shot'' queries to a database to retrieve similar images.
	Typically a K-nearest neighbor kind of algorithm is used, where weights
	measuring feature importance along each input dimension remain fixed
	(or manually tweaked by the user), in the computation of a given
	similarity metric. However, the similarity does not vary with equal
	strength or in the same proportion in all directions in the feature
	space emanating from the query image. The manual adjustment of these
	weights is time consuming and exhausting. Moreover, it requires a very
	sophisticated user. In this paper, we present a novel probabilistic
	method that enables image retrieval procedures to automatically capture
	feature relevance based on user's feedback and that is highly adaptive
	to query locations.  Experimental results are presented that
	demonstrate the efficacy of our technique using both simulated and
	real-world data.},
}