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

2004

@inproceedings{ChN2004,
	vgclass =	{refpap},
	author =	{YoungSik Choi and JiSung Noh},
	title =	{Relevance Feedback for Content-Based Image Retrieval Using
	Proximal Support Vector Machine},
	editor =	{Antonio Lagan\'{a} and Marina L. Gavrilova and Vipin Kumar
	and Youngsong Mun and C. J. Kenneth Tan and Osvaldo Gervasi},
	booktitle =	{Proceedings of the International Conference on
	Computational Science and Its Applications (ICCSA 2004)},
	address =	{Assisi, Italy},
	number =	{3044},
	series =	{Lecture Notes in Computer Science},
	pages =	{942--951},
	publisher =	{Springer-Verlag},
	month =	{May~14--17},
	year =	{2004},
	url =	{http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3044&spage=942},
	abstract =	{In this paper, we present a novel relevance feedback
	algorithm for content-based image retrieval using the PSVM (Proximal
	Support Vector Machine). The PSVM seeks to find the optimal separating
	hyperplane by regularized least squares. The obtained hyperplane
	comprises the positive and negative proximal planes. We interpret the
	proximal vectors on the proximal planes as the representatives among
	training samples, and propose to use the distance from the positive
	proximal plane as a measure of image dissimilarity. In order to reduce
	computational time for relevance feedback, we introduce the expanded
	sets derived from the pre-computed dissimilarity matrix, and apply the
	feedback algorithm to these expanded sets rather than the entire image
	database, while preserving the comparable precision rate. We
	demonstrate the efficacy of the proposed scheme using unconstrained
	image databases that were obtained from the Web.},
}