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

1999

@inproceedings{KeG1999,
	vgclass =	{refpap},
	vgproject =	{cbir},
	author =	{Trish Keaton and Rodney Goodman},
	title =	{A Compression Framework for Content Analysis},
	booktitle =	{IEEE Workshop on Content-based Access of Image and Video
	Libraries (CBAIVL'99)},
	address =	{Fort Collins, Colorado, USA},
	pages =	{69--73},
	month =	{June~22},
	year =	{1999},
	abstract =	{We present a statistical coding framework that supports
	content analysis and retrieval in the compressed domain. An
	unsupervised learning approach based on latent variable modeling is
	adopted to learn a collection or mixture, of local linear subspaces
	that are designed for compression, while providing a probabilistic
	model of the source useful for inferring image content. The compressed
	bitstream is organized to enable the progressive decoding of the
	compressed data, such that the bitstream is only decompressed up to the
	level necessary to satisfy the query. We describe methods of extracting
	relevant features from the compressed representation that support query
	based on single and multiple example images, high level class
	categories such as people, and low-level features like particular
	colors and textures. Retrieval experiments have shown that this
	representation provides good inferencing with very little
	decompression.},
}