An exploration of diversified user strategies for image retrieval with relevance feedback
Abstract
- Given the difficulty of setting up large-scale experiments with real users, the comparison
of content-based image retrieval methods using relevance feedback usually relies on the
emulation of the user, following a single, well-prescribed strategy. Since the behavior of
real users cannot be expected to comply to strict specifications, it is very important to
evaluate the sensitiveness of the retrieval results to likely variations of users’ behavior. It
is also important to find out whether some strategies help the system to perform
consistently better, so as to promote their use. Two selection algorithms for relevance
feedback based on support vector machines are compared here. In these experiments, the
user is emulated according to eight significantly different strategies on four ground truth
databases of different complexity. It is first found that the ranking of the two algorithms
does not depend much on the selected strategy. Also, the ranking of the strategies appears
to be relatively independent of the complexity of the ground truth databases, which
allows to identify desirable characteristics in the behavior of the user.
Reference
@article{jpt-jvlc08,
author = {Michel Crucianu and Jean-Philippe Tarel and Marin Ferecatu},
title = {An exploration of diversified user strategies for image retrieval with relevance feedback},
journal = {Journal of Visual Languages and Computing},
volume = {19},
number = {6},
year = {2008},
month = {December},
pages = {629--636},
publisher = {Elsevier},
address = {Amsterdam, The Netherlands},
note = {http://perso.lcpc.fr/tarel.jean-philippe/publis/jvlc08.html}
}
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