Abstract
Presents the theory, design principles, implementation and performance
results of PicHunter, a prototype content-based image retrieval (CBIR)
system. In addition, this document presents the rationale, design
and results of psychophysical experiments that were conducted to
address some key issues that arose during PicHunter's development.
The PicHunter project makes four primary contributions to research
on CBIR. First, PicHunter represents a simple instance of a general
Bayesian framework which we describe for using relevance feedback
to direct a search. With an explicit model of what users would do,
given the target image they want, PicHunter uses Bayes's rule to
predict the target they want, given their actions. This is done via
a probability distribution over possible image targets, rather than
by refining a query. Second, an entropy-minimizing display algorithm
is described that attempts to maximize the information obtained from
a user at each iteration of the search. Third, PicHunter makes use
of hidden annotation rather than a possibly inaccurate/inconsistent
annotation structure that the user must learn and make queries in.
Finally, PicHunter introduces two experimental paradigms to quantitatively
evaluate the performance of the system, and psychophysical experiments
are presented that support the theoretical claims.
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