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

2005

@article{JED2005,
	vgclass =	{refpap},
	author =	{Wei Jiang and Guihua Er and Qionghai Dai and Jinwei Gu},
	title =	{Hidden annotation for image retrieval with long-term
	relevance feedback learning},
	journal =	{Pattern Recognition},
	year =	{2005},
	note =	{(in press)},
	url =	{http://dx.doi.org/10.1016/j.patcog.2005.03.007},
	abstract =	{Hidden annotation (HA) is an important research issue in
	content-based image retrieval (CBIR). We propose to incorporate
	long-term relevance feedback (LRF) with HA to increase both efficiency
	and retrieval accuracy of CBIR systems. The work contains two parts.
	(1) Through LRF, a multi-layer semantic representation is built to
	automatically extract hidden semantic concepts underlying images. HA
	with these concepts alleviates the burden of manual annotation and
	avoids the ambiguity problem of keyword-based annotation. (2) For each
	learned concept, semi-supervised learning is incorporated to
	automatically select a small number of candidate images for annotators
	to annotate, which improves efficiency of HA.},
}