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

1996

@inproceedings{MiP1996,
	vgclass =	{refpap},
	vgproject =	{cbir},
	author =	{T. P. Minka and R. W. Picard},
	title =	{Interactive learning using a ``society of models''},
	booktitle =	{Proceedings of the 1996 IEEE Conference on Computer Vision and Pattern Recognition (CVPR'96)},
	address =	{San Francisco, California},
	pages =	{447--452},
	month =	{June},
	year =	{1996},
	url =	{ftp://whitechapel.media.mit.edu/pub/tech-reports/TR-349.ps.Z},
	abstract =	{Digital library access is driven by features, but features
	are often context-dependent and noisy, and their relevance for a query
	is not always obvious. This paper describes an approach for utilizing
	many data-dependent features in a semi-automated tool.  Instead of
	requiring universal similarity measures or manual selection of relevant
	features, the approach provides a learning algorithm for selecting and
	combining groupings of the data, where groupings can be induced by
	highly specialized and context-dependent features. The selection
	process is guided by a rich example-based interaction with the user.
	The inherent combinatorics of using multiple features is reduced by a
	multistage grouping generation, weighting, and collection process. The
	stages closest to the user are trained fastest and slowly propagate
	their adaptations back to earlier stages. The weighting stage adapts
	the collection stage's search space across uses, so that, in later
	interactions, good groupings are found given few examples from the
	user. Described is an interactive-time implementation of this
	architecture for semi-automatic within-image segmentation and
	across-image labeling, driven by concurrently active color models,
	texture models, or manually-provided groupings.},
}