This paper introduces a new approach called the ``customized-queries''
approach to content-based image retrieval (CBIR). 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 subclasses within the chosen major
class. This research 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. We apply this approach
to content-based retrieval of high-resolution tomographic images of patients
with lung disease and show that this approach yields 82.8% retrieval precision.
The traditional approach that performs retrieval using a single feature
vector yields only 37.9% retrieval precision.