  We present a novel approach that combines machine learning based interactive
image segmentation using supervoxels with a clustering method for the automated
identification of similarly colored images in large data sets which enables a
guided reuse of classifiers. Our approach solves the problem of significant
color variability prevalent and often unavoidable in biological and medical
images which typically leads to deteriorated segmentation and quantification
accuracy thereby greatly reducing the necessary training effort. This increase
in efficiency facilitates the quantification of much larger numbers of images
thereby enabling interactive image analysis for recent new technological
advances in high-throughput imaging. The presented methods are applicable for
almost any image type and represent a useful tool for image analysis tasks in
general.
