1999
@inproceedings{DBK1999,
vgclass = {refpap},
vgproject = {cbir},
author = {Dy, Jennifer G. and Brodley, Carla E. and Kak, Avi and Shyu, Chi-Ren and Broderick, Linda S.},
title = {The Customized-Queries Approach to {CBIR} using Using
{EM}},
booktitle = {Proceedings of the 1999 IEEE Conference on Computer Vision and Pattern Recognition (CVPR'99)},
address = {Fort Collins, Colorado, USA},
pages = {400--406},
organization = {IEEE Computer Society},
month = {June~23--25},
year = {1999},
url = {http://mow.ecn.purdue.edu/\~{}lrn/publications/cvpr99.pdf},
url1 = {http://mow.ecn.purdue.edu/\~{}lrn/publications/cvpr99.ps},
abstract = {This paper makes two contributions. The first contribution
is an approach called the ``customized-queries'' approach (CQA) to
content-based image retrieval. The second is an algorithm called FSSEM
that performs feature selection and clustering simultaneously. The
customized-queries approach first classifies a query using the features
that best differentiate the major classes and then customizes the query
to that class by using the features that best distinguish the images
within the chosen major class. This approach is motivated by the
observation that the features that are most effective in discriminating
among images from different classes may not be the most effective for
retrieval of visually similar images within a class. This occurs for
domains in which not all pairs of images within one class have
equivalent visual similarity, i.e. subclasses exists. Because we are
not given subclass labels, we must simultaneously find the features
that best discriminate the subclasses and at the same time find these
subclasses. We use FSSEM to find these features. We apply this approach
to content-based retrieval of high-resolution tomographic images of
patients with lung disease and show that this approach radically
improves the retrieval precision over the traditional approach that
performs retrieval using a single feature vector.},
}