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

1998

@inproceedings{TCL1998,
	vgclass =	{refpap},
	vgproject =	{cbir},
	author =	{Alexander Thomasian and Vittorio Castelli and Chung-Sheng Li},
	title =	{{CSVD}: Approximate Similarity Searches in High
	Dimensional Spaces Using Clustering and Singular Value Decomposition},
	editor =	{C.-C. Jay Kuo and Shih-Fu Chang and Sethuraman
	Panchanathan},
	booktitle =	{Multimedia Storage and Archiving Systems III (VV02)},
	address =	{Boston, Massachusetts, USA},
	volume =	{3527},
	series =	{SPIE Proceedings},
	pages =	{144--154},
	month =	{November},
	year =	{1998},
	note =	{(SPIE Symposium on Voice, Video and Data Communications)},
	abstract =	{Many data-intensive applications, such as content-based
	retrieval of images or video from multimedia databases and similarity
	retrieval of patterns in data mining, require the ability of
	efficiently performing similarity queries. Unfortunately, the
	performance of nearest neighbour (NN) algorithms, the basis for
	similarity search, quickly deteriorates with the number of dimensions.
	In this paper we propose a method called Clustering with Singular Value
	Decomposition (CSVD), combining clustering and singular value
	decomposition (SVD) to reduce the number of index dimensions. With
	CSVD, points are grouped into clusters that are more amenable to
	dimensionality reduction than the original dataset. Experiments with
	texture vectors extracted from satellite images show that CSVD achieves
	significantly higher dimensionality reduction than SVD alone for the
	same fraction of total variance preserved. Conversely, for the same
	compression ratio CSVD results in an increase in preserved total
	variance with respect to SVD (e.g., a 70\% increase for a 20:1
	compression ratio). Then, approximate NN queries are more efficiently
	processed, as quantified through experimental results.},
}