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.