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

2000

@inproceedings{TWH2000,
	vgclass =	{refpap},
	author =	{Qi Tian and Ying Wu and Thomas S. Huang},
	title =	{Incorporate Discriminant Analysis with {EM} Algorithm in
	Image Retrieval},
	booktitle =	{Proceedings of the IEEE International Conference on
	Multimedia and Expo (ICME 2000)},
	pages =	{299--302},
	month =	{30~July--2~August},
	year =	{2000},
	url =	{http://www.ece.northwestern.edu/\~{}yingwu/papers/ICME00.pdf},
	abstract =	{One of the difficulties of Content-Based Image Retrieval
	(CBIR) is the gap between high-level concepts and low-level image
	features, e.g., color and texture.  Relevance feedback was proposed [1]
	to take into account of the above characteristics in CBIR. Although
	relevance feedback incrementally supplies more information for fine
	retrieval, two challenges exist: (1) the labeled images from the
	relevance feedback are still very limited compared to the large
	unlabeled images in the image database. (2) relevance feedback does not
	offer a specific technique to automatically weight the low-level
	feature. In this paper, image retrieval is formulated as a transductive
	learning problem by combining unlabeled data in supervised learning to
	achieve better classification. Experimental results show that the
	proposed approach has a satisfactory performance for image retrieval
	applications.},
}