  Domain-specific image collections present potential value in various areas of
science and business but are often not curated nor have any way to readily
extract relevant content. To employ contemporary supervised image analysis
methods on such image data, they must first be cleaned and organized, and then
manually labeled for the nomenclature employed in the specific domain, which is
a time consuming and expensive endeavor. To address this issue, we designed and
implemented the Plud system. Plud provides an iterative semi-supervised
workflow to minimize the effort spent by an expert and handles realistic large
collections of images. We believe it can support labeling datasets regardless
of their size and type. Plud is an iterative sequence of unsupervised
clustering, human assistance, and supervised classification. With each
iteration 1) the labeled dataset grows, 2) the generality of the classification
method and its accuracy increases, and 3) manual effort is reduced. We
evaluated the effectiveness of our system, by applying it on over a million
images documenting human decomposition. In our experiment comparing manual
labeling with labeling conducted with the support of Plud, we found that it
reduces the time needed to label data and produces highly accurate models for
this new domain.
