Thomas P. Minka, Rosalind W. Picard

Abstract

Digital library access is driven by features
but features are often contextdependent and noisy
and their relevance for a query is not always obvious This paper describes an approach for utilizing many datadependent
userdependent
and taskdependent features in a semiautomated 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 spe cialized and contextdependent features The se lection process is guided by a rich examplebased interaction with the user The inherent com binatorics of using multiple features is reduced byamultistage 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 stages search space across uses
so that
in later interactions
good groupings are found given few examples from the user Described is an interactivetime imple mentation of this architecture for semiautomatic withinimage segmentation and acrossimage la beling
driven by concurrently active color mod els
texture models
or manuallyprovided group ings
Issues for digital libr