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

2004

@article{GHP2004,
	vgclass =	{refpap},
	author =	{Iker Gondra and Douglas R. Heisterkamp and Jing Peng},
	title =	{Improving image retrieval performance by inter-query
	learning with one-class support vector machines},
	journal =	{Neural Computing \& Applications},
	year =	{2004},
	note =	{(in press)},
	url =	{http://dx.doi.org/10.1007/s00521-004-0415-2},
	abstract =	{Relevance feedback (RF) is an iterative process which
	improves the performance of content-based image retrieval by modifying
	the query and similarity metric based on the users feedback on the
	retrieval results. This short-term learning within a single query
	session is called intra-query learning. However, the interaction
	history of previous users over all past queries may also be potentially
	exploited to help improve the retrieval performance for the current
	query. The long-term learning accumulated over the course of many query
	sessions is called inter-query learning. We present a novel RF
	framework that learns one-class support vector machines (1SVM) from
	retrieval experience to represent the set memberships of users
	high-level concepts and stores them in a concept database. The concept
	database provides a mechanism for accumulating inter-query learning
	obtained from previous queries. By doing a fuzzy classification of a
	query into the regions of support represented by the 1SVMs, past
	experience is merged with current intra-query learning. The geometric
	view of 1SVM allows a straightforward interpretation of the density of
	past interaction in a local area of the feature space and thus allows
	the decision of exploiting past information only if enough past
	exploration of the local area has occurred. The proposed approach is
	evaluated on real data sets and compared against both traditional
	intra-query-learning-only RF approaches and other methods that also
	exploit inter-query learning.},
}